Carolina Guevara
email: carolina.guevara@epn.edu.ec

Stéphane Riou

Corinne Autant-Bernard

Agglomeration externalities in Ecuador. Do urbanisation and tertiarisation matter?

Keywords: Agglomeration externalities, productivity, urbanisation, tertiarisation, Latin America O54, L80, O18, L60 agglomeration externalities, productivity, urbanisation, tertiarisation, Latin America O54, L80, O18, L60

The paper investigates whether the tertiarisation and the rapid urbanisation faced by developing countries favour agglomeration economies. Focusing on Ecuadorian cantons, a productivity equation is estimated using the GMM model with instruments controlling for endogeneity. The varying impact of industrial concentration, diversity, competition and density across industries is investigated and for the first time, the implication of the level of urbanisation on agglomeration externalities is studied. Stronger effects are found for services. The threshold of urbanisation at which diversity, density and competition agglomeration externalities all generate positive effects was 33% while they seem challenged by congestion in highly urbanised cantons.

Introduction

The theoretical and empirical literature on agglomeration is extensive. The channels by which agglomeration economies translate into economic efficiency have been widely debated (see blackRosenthal and Strange, black2008), stressing the potential role played by local diversity, specialisation and competition.

However, the survey by blackDe Groot et al. (black2015) points to the fact that at-tempts to understand these various types of agglomeration economies mainly focus on developed countries, despite that the nature of the agglomeration externalities in developing countries seems to be a relevant issue. Indeed, one can expect agglomeration to have a differentiated effect on developing countries in comparison with developed ones because of the different conditions under which these agglomeration externalities take place. Actually, at the country level, the literature does not provide clear evidence of the existence of a monotonic relationship between sectoral diversification and income. blackImbs and Wacziarg (black2003) detected a robust U-curved relationship between specialisation and GDP per capita. Interpreting this relationship as a causality, diversification could be a major driving force of economic development for countries at intermediate stages of development.

A better understanding of the driving forces at work behind agglomeration in developing countries is therefore requested in order to identify the sectoral organisation that maximizes its productivity effects. Indeed, identifying whether specialisation, diversity and competition accelerate or impede local growth contains some important public policy issues. To this aim, it is important to identify the impact of the industrial structure on firms' performances at a local level. Indeed, it can be questioned whether industrial diversity, which seems to have positive effects on macroeconomic performance, generates similar effects on a more spatially disaggregated level. Income inequality between regions is a significant concern in developing countries, particularly in Latin America. Their industrial structure is frequently presented as part of the explanation for the economic gap between regions. Poor regions suffer from an excess of specialisation, the firms located therein having too few opportunities to benefit from inter-industry relationships. The immediate policy recommendation would be to encourage the emergence of an industrial diversification dynamics in these regions. Such recommendation must be systematically based on robust empirical evidences that the industrial structure of regions has a significant impact upon local growth.

A rising literature tries to fill this gap but there is still little empirical evidence for developing countries at the regional or individual level (see. blackCombes and Gobillon (black2015), blackChauvin et al. (black2014)). In most cases, evidence is provided on the impact of both specialisation (often measured through industrial concentration) and diversity, and when studied, on the competition effect. As detailed below, the underlying mechanisms remain however poorly explored so far. At the regional level, blackGhani et al. (black2013) point to a higher productivity in Indian districts that display a positive role played by the initial level of specialisation for employment growth. For the Indonesian case, blackWidodo et al. (black2015), based upon firm-level data, observe that local specialisation increases firm's technical efficiency while diversity would reduce it. blackErcole and O'neill (black2016) stress the role of industrial linkages, pointing to the positive impact of related variety for Indonesia. In the Chinese case, blackNing et al. (black2016) put special emphasize on the role of agglomeration effects on the level and spillovers effects of FDI. Their results reveal positive impact of specialisation, diversity and competition. As for the Latin American case, blackDa Silva Catela et al. (black2010) show that both diversity and specialisation have positive effects on the real average wage per worker at the municipality level in Brazil. In Chile, blackSaito and Gopinath (black2009) found that more productive firms in the food industry locate in regions where other food industry plants agglomerate and in regions that combine industrial diversification with a large market.

More recently, blackSimões and Freitas (black2014) and blackBarufi et al. (black2015) explore, respectively, the drivers of agglomeration economies on regional and individual wages in Brazil. They both find mixed evidence of specialisation-based and diversitybased agglomeration forces although specialisation prevails in the study conducted by blackBarufi et al. (black2015).

These investigations focus however on the manufacturing industry only (blackBarufi et al. (black2015) being however a noticeable exception). The rising share of service sector in developing economies (reaching more than 50% for instance in Latin America) leads to seek a better understanding about how the change of industrial structure in these countries may shape agglomeration economies. For developed countries, black-Combes (black2000) shows that the impact of the local economic structure differs in industry and services: while employment density, competition, sectoral specialization and diversity have a negative impact on growth in manufacturing industries, diversity and density exhibit positive effects on service industries. If the same picture occurs in developing countries, this means that the tertiarisation of these countries would reinforce their agglomeration economies.

In addition, one of the most striking fact in developing countries is the drastic increase in the level of urbanisation and, compared to developed countries, developing countries face huge disparities in terms of urbanisation rate at the regional level. Such features are also likely to shape agglomeration economies. The intuition is twofold. First, a critical mass of urban population may be required to enjoy the potential benefits of agglomeration. A certain level of urbanisation would guarantee the existence of minimum transport and telecommunication infrastructures and banking, financial services or other specific services that matter for the emergence of externalities (blackHenderson, black2000). Secondly, one may expect the existence of an optimal city size which maximizes the economy's per capita output net of congestion losses (blackBertinelli and Black, black2004). Therefore, in a context of increasing spatial concentration of economic activities, a major challenge for developing countries is to reinforce the role of their urban systems as drivers of economic growth (blackDuranton, black2015). It is thus worth investigating whether agglomeration economies vary according to the local level of urbanisation.

In order to explore these issues, this paper studies the emergence and nature of agglomeration effects in the Latin American context. Total factor productivity is regressed on specialisation, diversity and competition indexes while controlling for the effect of density.In order to explore the underlying features that condition the emergence and nature of agglomeration effects, separate estimates of the agglomeration effects in the service and manufacturing industries are provided. Whether a critical threshold of urbanisation would be required in order to benefit from agglomeration effects is then assessed.

Empirical evidence is driven from Ecuadorian cantons. Indeed, Ecuador constitutes a suitable case study as this country shares numerous common characteristics with most of the Latin American countries, such as its industrial composition and its urbanisation process. In Ecuadorian regions, 25% of the total economic activity take place in the primary activity sector (22% in average in Latin America), 20% in the manufacturing sector (resp. 22% in Latin America) and 55% in the service industry (resp. 57% in Latin America). Most importantly, the services industry in Ecuador recorded an average growth rate of 1.5% (resp. 0.4% in Latin America) in the 2000 decade while the manufacturing industry recorded a 0.9% (resp. 0.5% in Latin America). This shows that Ecuador might be experiencing a process of tertiarisation. The urbanisation process is also similar to the Latin American one. Due to the fact that emerging countries are far from being fully urbanised, the urbanisation growth is rapid (blackHenderson, black2003). The annual average growth rate of urbanisation is around 2,2% in Latin America and around 3,2% in Ecuador.

In addition, when measuring agglomeration economies, one concern is endogeneity. This problem is not systematically addressed in the studies mentioned above (black- Combes and Gobillon, black2015). In this study, one major source of endogeneity that must be controlled is the existence of circular causality between local productivity and the type of externalities estimated. In this purpose, the instrumental variables method is implemented. Estimates are provided using the GMM model and instrument the endogenous variables with a threat index, long lagged population density and spatial lags. This empirical work is mainly based on the 2010 Economic Census of Ecuador which contains information declared in 2009. By aggregating company data, a two-digit industry database is built on the cantonal level. Based on 7,803 canton and industry pairs, our results support Porter's externalities which argue that the combination of a diverse industrial environment and strong competition leads to positive productivity effects. In this line, specialisation is then detrimental. The magnitude of the externalities from diversity is positive and significant in the service industry, pointing to higher agglomeration forces when tertiarisation raises. Economies of density, measured by the density of firms and employees at the canton and industry levels, are found for the manufacturing sector. Competition leads to local productivity gains, especially for manufacturing sectors. More importantly, the regressions highlight the fact that diversity-based externalities increase with the level to which cantons are urbanised. In contrast, the effects of density and competition are non-monotone across the levels of urbanisation, producing both positive and negative effects. The latter suggest the presence of congestion diseconomies in some cantons.

The rest of the paper is organised as follows. Section 2 presents the various indicators used to identify the different types of externalities and provides statistical facts highlighting the sectoral and spatial organisation of economic activities in Ecuador. Section 3 focuses on the specifics of the model and the econometric methodology. The results are discussed in Section 4, separating first the service and manufacturing industries and distinguishing then the effects according to the levels of urbanisation. The main conclusions are presented in Section 5.

2 Spatial and sectoral distribution of economic activities in Ecuador

Measures of agglomeration externalities

The 2010 Economic Census of Ecuador includes information on 511,130 firms. It covers all localities with more than 2,000 inhabitants, with the exception of rural areas. By aggregating firm data 1 , an industrial database is constructed with two-digit ISIC industries (International Standard Industrial Classification). The resulting sample data covers 78 industries including manufacturing and service activities (Appendix A). Despite the availability of data on a very disaggregated geographical scale, using information on the block and city levels would induce too many missing industries. For this reason, data are aggregated at the cantonal level. Ultimately, the resulting panel data covers 78 industries and 221 cantons. Even after this aggregation, however, some industries are not present in all cantons. More specifically, on average 42 industries are absent in each canton.

The average performance of a given sector in a given canton is measured by the total factor productivity. Alternatively, we could restrict our analysis to the labor productivity 2 . It would result in an incomplete picture of the sectoral efficiency sources as a large number of sectors under consideration are capital intensive. Moreover, it is well known that some externalities directly translate into efficiency gains in the use of capital. For instance, sectoral diversity offers a better productive environment by enlarging the inputs market and improving the matching with the needs of firms. The [START_REF] Levinsohn | Estimating production functions using inputs to control for unobservables[END_REF]'s method is used. Following this method, the productivity measure is consistent and the simultaneity problem between production and the unobservable productivity is correctly treated. To estimate productivity, firm level data is first considered for the following production function of firm i.

ln rev i = ln L i β L + Φ(ln K i , ln inp i ) + i (1)
where rev i is the level of revenue, L i is the number of employees, K i is the assets value and inp i is the materials value (including electricity, packaging, raw materials, etc.) of firm i. The term Φ is approximated with a polynomial in (K i , inp i ) capturing the TFP. In order to be consistent with the level of data availability for the other variables, the resulting TFP at the firm level is averaged by industry and canton.

The Marshall-Arrow-Romer (MAR) view of externalities considers that the productive efficiency of a particular industry in a given region is mainly boosted by the possible interactions within the industry in this region. Put differently, spatial proximity facilitates within industry knowledge transmission and technological appropriation. As it is standard in the literature, a relative specialisation index for each pair of sectorcanton is built (see. blackGlaeser et al. (black1992); blackBeaudry and Schiffauerova (black2009)). The index measures the share of the canton's employment devoted to the given industry. Note that this measure is the regular location quotient which describes the industrial concentration. To control for the size of the sectors at the canton level and identify to what extent a canton is concentrated in a given sector compared to the country, it is normalised to the share of national employment allocated to this sector. The industrial concentration index is thus as follows:

spe s,c = empl s,c /empl c empl s /empl (2)
where empl s,c is the employment in sector s in canton c and empl the national employment.

Unlike MAR theory, blackJacobs (black1969) sees industrial diversity of a particular region as the main source of externalities. To capture the degree of sectoral diversity faced by sector s in canton c, the inverse of the Herfindahl index is built. Formally:

div s,c = 1 S s =1,s =s (empl s ,c /(empl c -empl s,c )) 2 (3)
where S is the number of industries. When industries are numerous and of equal size in a region, the value of this index reaches its maximum. Based on this measure, diversity externalities are understood as the effects on productivity due to the presence of different industries 3 . In addition, it is worth noting that industrial concentration and diversity calculated in this way are not necessarily opposed to one other. A canton may host a very large share of a sector and a broad number of other industries as well. Such a canton is therefore both specialised and diversified (blackPanne and Beers, black2006).

In accordance to MAR theory, blackPorter (black2010) argues that specialisation is the main source of externalities but that the market structure matters. Unlike a local monopoly, competition produces emulation and fosters the adoption of the best organization and/or technologies. To approximate these competition effects, full use of the firm level data is made. This enables characterising the size distribution of firms within sector s in canton c. After a normalisation at the country level, we have:

comp s,c = 1/ N i (empl i,s,c /empl s,c ) 2 1/ N i (empl i,s /empl s ) 2 (4)
where empl i,s,c is the employment of firm i belonging to sector s and located in canton c, and empl i,s is the employment of firm i in sector s at the country level. It is worth noting that this measure of competition is close to the industrial concentration measure. However, the latter shows the employment concentration in given industries, whereas competition shows the strength of firms within an industry that allows them obtaining productivity gains through innovation.

Moreover, abundant and recent literature provides evidences on the effect of density on local productivity (see blackCombes and Gobillon, black2015). Most existing studies use employment density as an explanatory variable. Formally, it is computed as follows:

empl den s,c = empl s,c /area c empl s /area (5)
Observe that a high employment density value may reflect both the presence of few big firms or the existence of a large number of competitive firms. Both configurations may produce positive externalities as they may emerge from both individual interactions in a given firm or between firms. Consequently, we provide the following alternative index which measures density of operating firms in sector s and canton c:

den s,c = f s,c /area c f s /area (6)
where f s,c is the number of firms in sector s located in canton c and area c is the area of canton c in square kilometres. At the denominator, f s is the number of firms of sector s and area is the total surface of the country.

Spatial statistics of Ecuador

The dynamism of the Ecuadorian economy lies in its large share of service and manufacturing industries, which in 2010 represented 20% and 55% of national production, respectively. Such an economic structure is common to other developing countries in Latin America (blackHernández et al., black2016;blackECLAC-ILO, black2012). This study thus contributes to a better understanding of agglomeration externalities in these countries.

In Ecuador, the share of population living in urban areas was 67.8% in 2010. However, this share hides strong intra-regional heterogeneity. In terms of cantons, the proportion of the population living in urban areas is, on average, 38.9%. This means that there are many more cantons with rural population than cantons with urban population. Figure 1 presents the spatial concentration of economic activities in cantons using firms density (a) and employment density (b). The darker areas correspond to denser cantons. It can be observed that spatial patterns are fairly similar across density measures. Denser cantons are mainly located in the Sierra region of the country, while the less dense cantons are located in the eastern region which has large territories occupied by the rain forest. On the national average, the mean (employment) density of firms in cantons is 3.9 firms (12 workers) per square kilometre and the standard deviation is 13.35 (40), which indicates high heterogeneity between cantons. In the north, the main centre is the capital, Quito (1 in Figure 1), which records a density of 23 firms (121 workers) per square kilometre. Three centres can be distinguished in the coastal region: Guayaquil (2 in Figure 1), Manta (3 in Figure 1) and Machala (6 in Figure 1) which have densities of 20 (100), 28 (121) and 32 (117) firms (workers) per square kilometre, respectively. Another centre of economic activity is Ambato (4 in Figure 1), recording 18 firms (59 workers) per square kilometre. In the south, the main centre is Cuenca (5 in Figure 1) which has a density of nine firms (36 workers) per square kilometre. In addition, Figure 1 shows higher firm and employment densities in cantons which are close to the economic centres.

The two following figures present the profile of cantons in terms of industrial diversity and productivity. 2a and2b indicates a similarity: ceteris paribus, denser cantons exhibit a high level of industrial diversity. Moreover, the closer the cantons are to urban centres, the higher their level of diversity. As expected, the three major urban centres have higher productivity rates than the national average: the capital, Quito (1 in Figure 1), Guayaquil (2 in Figure 1) and Cuenca (5 in Figure 1). The correlation between diversity and density at the cantonal level is 0.24, which is significant. Similarly, density and productivity are correlated, with a significant coef- ficient of 0.15. Overall, highly diversified cantons are larger in terms of economic size (blackHenderson, black1997). As shown in Figure 3, larger cantons present a wider variety of economic activities. The correlation coefficient between size, measured by total employment, and diversity index is 0.5087.

Basic comparison between Figures

3 Econometric methodology

Econometric issues related to agglomeration economies

In productivity regressions involving agglomeration variables, endogeneity problems may emerge for two reasons (blackCombes and Gobillon, black2015). On the one hand, endogeneity arises when omitted variables affect both agglomeration and productivity.

For instance, a high level of productivity in a given industry might be caused by good local conditions, such as transport infrastructures, services and other facilities, which, in turn, increase the level of agglomeration in the region. On the other hand, endogeneity can emerge from a bidirectional causality, when local agglomeration is determined by local productivity and, in turn, determines it. In this study, endogeneity concerns can arise from the industrial concentration variable. High sectoral productivity in one canton is likely to attract more firms from the same sector to that canton. This encourages more externalities from specialisation, which will be reflected in higher productivity. As a consequence of the bidirectional nature of the relationship, the variable encompassing the industrial concentration will be endogenous.

In terms of externalities from diversity, the impact of local productivity in a particular industry on the level of industrial diversity of one canton is, a priori, not obvious. Typically, it depends on whether the productivity gains of a given industry which spill-over across sectors are such that they boost their attractiveness. If so, local productivity in a given industry would lead to a more diversified local economy. In the other direction, according to Jacobs, a more diverse industrial environment is beneficial for the exchange of ideas across industries and the emergence of innovations, which, in turn, foster productivity gains. Consequently, the endogeneity of local industrial diversity by reverse causality remains to be tested.

Regarding local industrial competition, the externalities arising from competition described by Porter may boost innovation within the same industry, but the opposite direction of the relationship is unclear. Local industrial productivity may boost competition through the entry of new firms attracted by the local profitability of existing firms. However, firms would rather locate in places where competition is not strong. Hence, the endogeneity of this variable also needs to be tested.

Specification of the model

In terms of the aforementioned issues of endogeneity, the feasible efficient two-step GMM estimator is used in the regressions. This estimator is efficient (more than 2SLS estimator 4 ) in the presence of heteroskedasticity. Thus, it provides consistent estimates by dealing with both potential endogeneity and heteroskedasticity.

The model estimating the effect of each type of externality upon the productivity level of a specific industry s in a given canton c is as follows.

(T F P s,c ) =α + β 1 spe s,c + β 2 div s,c + β 3 comp s,c + β 4 den s,c + γ 2 p 2 + ... + γ l p l + λ 2 s 2 + ... + λ l s S + selection s,c + u s,c (7)
where (T F P s,c ) is the Total Factor Productivity in industry s in canton c, estimated using the blackLevinsohn and Petrin (black2003)'s method. To control for unobserved heterogeneity, l-1 province fixed effects given by p 2 ...p l are introduced as well as sector fixed effects given by s 2 ...s S . The term selection s,c is a control variable which allows us to address the issue regarding the presence of many zeros in the database (all cantons do not account for the same number of economic activities). This is the inverse of Mill's ratio, computed using the estimates of a selection equation (see Appendix blackC) proposed by blackHeckman (black1979).

In order to correct the endogeneity issue derived from the circular causality of the endogenous variables, a set of instruments Z i , assumed to be uncorrelated with the error (cov(Z i , u s,c ) = 0), is used.

First, the 1990 population density (pop 90 ) is used as an instrument. The long-term lagged variable is useful to remove the simultaneity that arises from contemporaneous effects. It is unlikely to be correlated with the error term at time t since it was generated at an earlier point in time (blackCombes et al., black2010). The endogenous variables are also instrumented with their spatial lags (spe spatial, comp spatial, den spatial, div spatial). In order to compute the spatial lags, the inverse distance weight matrix is built using the coordinates of the capital of each canton. The spatial lags are likely to be relevant instruments since neighbouring cantons could affect one canton's level of industrial concentration, diversity, density or competition, but the extent to which neighboursâ agglomeration externalities could affect the level of productivity of one canton cannot be immediately assumed. It is worth noting that the introduction of the spatial dimension of these instruments somehow controls for possible spatial correlation in the model. The squares of the endogenous variables could be considered as good instruments because they explain the current patterns of endogenous variables. However, only the density squared (den 2 ) passes the validity test.

Another interesting instrument is the natural threat index of cantons (threat) which takes into account causes of natural disasters in each canton. This variable has been compiled on the basis of a joint research study produced by the System of Economic Indicators (SIISE), Oxfam International and Cooperazione Internazionale (COOPI). This index is assumed to be a reasonable instrument because it is correlated with all the endogenous variables. The level of industrial concentration, diversity and density of regions is related to their degree of risk exposure.

In order to have consistent estimates of the productivity equation (black7), the instrumental variables must meet two specific properties. First, they must be highly correlated with instrumented variables (relevance condition), and second, a valid instrument has to be exogenous (orthogonality condition). Although the latter cannot be directly tested, the former is readily testable through the following analysis of correlation coefficients between the endogenous variables and their instruments shown in Table 1. In parentheses, the p-value of the correlation coefficient (* p < 0.05) and the p-value of the C-statistic.

Threat index, 1990 population density and squared density are highly correlated with all the endogenous variables, proving to be relevant instruments. The spatial lags of diversity, competition and density are correlated with some of the endogenous variables. In contrast, the spatial lag of industrial concentration is not correlated with any of the endogenous variables. Therefore, it is not considered as instrument.

The orthogonality condition is tested after the estimations through the C statistic or so called 'difference in Sargan' statistic whose null hypothesis is that suspected and additional instruments are valid. As shown in Table 1, the null hypothesis cannot be rejected, so instruments are valid. firms in their own field or even in other fields are doing and the process of imitation and cross-fertilisation takes place. By contrast, negative effects of both specialisation and diversity-based externalities arise in the manufacturing industry. As recent works show, the effect of agglomeration externalities would depend on the life cycle of the industry (blackBoschma et al., black2005;blackGreunz, black2004;blackForni and Paba, black2002). In this case, it seems that the manufacturing firms in Ecuador are producing standard and mature products. In that sense, neither specialisation nor diversity externalities favour their level of productivity. This could be due to the low quality of transport infrastructure and low levels of innovation impeding to generate significant MAR and Jacobs' externalities.

Regarding the effect of competition, results confirm the existence of Porter externalities. A high level of sectoral competition at the canton level in any sector leads to productivity gains. The positive effect is particularly strong in the manufacturing sector suggesting that local competition is more strongly transformed into emulation and productivity gains in this sector.

As in the great majority of papers devoted to the estimation of agglomeration economies, the local density of firms boosts productivity (blackMelo et al., black2009). For Ecuador, the elasticity of productivity with respect to density is significant in the manufacturing sector only, where it reaches around 5.2%. While international comparisons must be interpreted very cautiously because of different methodological approaches, blackCiccone and Hall (black1996) and blackCiccone (black2002) found that economies of density in Europe and the US measured by the effects of employment density are 4.5% and 5%, respectively. The results obtained for developing countries are usually higher, ranging between 5% and 11%. In particular, according to blackMelo et al. (black2009)'s meta analysis, South American countries tend to experience around 5.6% higher density effects than Europe. Although, Ecuador achieved a 5.2% elasticity in 2010 according to our results, further investigation for recent years is needed. Other South American countries confirm higher estimated productivity elasticities to density such as Brazil with 11% (blackHenderson, black1988;blackDa-Mata et al., black2007), Chile with 0.09 (blackSaito and Gopinath, black2009) and in Colombia (blackDuranton, black2016) with 0.05 (elasticity of wages to respect to population). There may be specific mechanisms operating in these countries which induce higher levels of labour productivity, such as the demographic bonus and internal migration whose complementary nature between unskilled and skilled workers produces positive effects (blackCombes et al., black2013).

Does urbanisation matter?

The preceding sections allow accurately observing the existence of agglomeration benefits for industries in Ecuador's cantonal economies. However, one of the specific feature of developing countries is their strong regional heterogeneity in terms of urbanisation. Ecuador is no exception: in terms of urbanisation, the distribution of cantons is skewed (see figure 4). The second quartile is 0.33, i.e., there are 110 cantons of a total of 221 cantons with an urban population which is lower than 33%.

Given the heterogeneity of Ecuadorian cantons, it is worth investigating whether the externalities identified in the preceding sections are shaped by the level of urban- isation (urban rate) of the cantons. The intuition is twofold. First, a critical mass of urban population would be required to enjoy the potential benefits of agglomeration. Secondly, one may expect the existence of an optimal city size which maximizes the economy's per capita output net of congestion losses (blackBertinelli and Black, black2004).

Through an analysis which disaggregates the entire sample into different levels of urbanisation, measured by the share of urban settlements 8 , light is shed on two aspects: i. the relationship between the magnitude of externalities and the level of urbanisation and ii. the threshold of urbanisation at which agglomeration externalities decline.

In order to determine the threshold values, the level of urbanisation is first set at 33%, which is the median of the sample. Some simulations have then been run changing the level of urbanisation and identify two threshold values: 46% and 61%, at which the significance of agglomeration externalities change. In Table 4, the estimates for different levels of urbanisation are presented: lower than 33% (column1); larger than 33% (column 2); larger than 46% (column 3) and larger than 61% (column 4).

Firstly, urbanisation mainly alters the effects of diversity and density externalities. The sensitivity of these variables could be due to their relationship with urban systems. Cities host a great diversity of industries and density of activities. The effect of competition also changes throughout the urbanisation process. In contrast, the effects of specialisation externalities measured through our industrial concentration index slightly change with levels of urbanisation.

The results in column (1) of Table 4 indicate that when a region faces low levels of urbanisation, externalities from diversity and density economies do not occur. This could be explained by the fact that regions with low levels of urbanisation do not provide an appropriate environment for the interaction of different industries since communication and transport infrastructures are still embryonic and the provision of services is scarce. By contrast, the competition is a significant source of positive externalities at low urbanised cantons. As these cantons are small, monopolist firms may not be interested in locating there. Then, knowledge and information could be diffused more easily in small cantons so that high competition can promote positive externalities.

On the other hand, the results suggest that externalities from industrial concentration negatively affect cantons at low and medium levels of urbanisation. The negative effect might arise from the primary sector which represent a large share of the economic activity at those levels of urbanisation.

The results in column (2) suggest that as a canton urbanises, the conditions for diversity externalities improve. A more urbanised environment can benefit from the concentration of different industries for many reasons. First, industrial dynamism is improved through the provision of services and in turn productivity is enhanced. Second, urban areas benefit from better communication and transport infrastructures, which facilitate cooperation, cross-fertilisation of knowledge between firms of different industries. In addition, an urbanised environment offers a larger variety of schools and universities which enhances the recruitment of more skilled workers and facilitates job turnover between industries.

Simulations show that at higher levels than 33%, the density effect becomes positive and significant. However, we can assume that there is a certain level of urbanisation leading to negative effects of firm density on productivity because of congestion diseconomies in big cities. In order to test this hypothesis, the model was run with several levels of urbanisation above 33% and the positive effect of density ceases to be significant at 61% urbanisation rate (fourth column). It turns to be negative and significant at high levels or urbanisation. This result shows clear evidence of congestion effects in larger agglomerations.

The externalities derived from competition are no longer positive for levels of urbanisation higher than 46%. They become significantly negative in cantons with levels of urbanisation above 61%. This negative effect means that competition may become detrimental for firm productivity. As competition heightened, land costs and input prices increase, while firms' market power decreases, inducing lower productivity for firms located in highly urbanised environment.

Conclusions

Estimating a productivity equation using the GMM model with instruments to control for endogeneity, we investigate whether the tertiarisation and the rapid but uneven urbanisation faced by developing countries would favour agglomeration economies.

The results show that diversity externalities matter more than specialisation externalities, supporting Jacobs' externalities. The strong presence of diversity externalities coming from the concentration of different industries is explained by the fact that most industries in the country interact with one another, leading to high backward and forward linkages. Therefore, the exchange and mixture of ideas from different industries is facilitated in such a diverse industrial environment. In addition, the innovation-inducing effect of competition promulgated by Porter's theory appears to be relevant not only at the general level but also at the sectoral level. Conversely, cantons and industries do not benefit from localisation economies. This means that there is, in fact, rivalry between diversity and specialisation externalities. In terms of the economies of density, measured by firm spatial concentration, the es-timated elasticity of productivity with respect to density is 5% on the cantonal and industrial scale.

More importantly, these agglomeration economies are sensitive to the rising share of service industries in developing countries and to the increase in their urbanisation level. The particularities of the industries determine the specific effects of agglomeration externalities on their productivity. The service industry benefits the most from the agglomeration of different industries. There might be two reasons for that: on one hand, their output is not devoted to a specific industry, and on the other hand, knowledge spillovers in this industry are very likely to be due to a wide sales network. The nature and therefore the magnitude of agglomeration forces would thus evolve as service industries rise. The manufacturing industry benefits more from the geographical proximity of firms and workers within the same industry. The increasing share of service industry in developing countries, boosted by agglomeration economies, enlarges in return agglomeration economies.

For the first time, this study presents the impact of the level of urbanisation upon agglomeration externalities. The results show that no significant diversity effect is recorded in cantons whose urbanisation level is under 33%. As a canton urbanises, several changes occur. The threshold at which diversity, competition and density agglomeration externalities generate positive effect was identified at an urbanisation rate of 33%. At higher rates of urbanisation than 46%, the positive effect of competition disappears, which means that in highly urbanised cantons, congestion effects would prevail.

Our results may have interesting implications for policy makers. Urbanisation should be supported as long as it increases agglomeration economies, and public infrastructures should be developed to reduce the risk of congestion effects. Moreover, as service industries develop, diversity would become a more prevailing agglomeration force. Public actors could thus accompany the tertiarisation of the economy with support for the diversification of local activities in order to strengthen this prevailing source of agglomeration economies.

It is worth noting however that additional research investigations would be requested to assess congestion effects, to observe more recent years and to take into account the dynamic nature of the urbanisation and tertiarisation processes. In addition, due to a large amount of missing values at the firm level, very few variables were available to explain the variability at the firm level, preventing to run reliable estimations on individual data. As a consequence, this study relies on an empirical strategy based on aggregated data. Even if blackMelo et al. (black2009)'s meta analysis shows that there is no difference between aggregated and individual data results, future researches should try to get individual data to grasp more precisely agglomeration economies.

Notes

1 Another strategy consists in using firm-level data. However, some variables at the firm level, needed to explain the individual variability, are generally missing. Thus, only one variable (lifetime) is available to explain the variability at the firm level. Consequently, such a strategy is not adequate for econometric reasons.

2 Previous versions of the article focuses on the labor productivity. They are available upon request 3 Note that diversity externalities can also be derived from the so called Related Variety, which is the interaction between sub-industries within one industry (blackFrenken et al., black2007). In the present study, evidence on the diversity externalities originated in the interchange between different industries is provided. However, estimations using Related variety measures were also conducted. Two measures were tested: i) RV at the cantonal and sectoral level based on blackFrenken et al. (black2007) and ii) RV at the cantonal and sectoral level based on the standard measure. Due to the sensitivity of the results to the measure used (Frenken vs Standard), they are not presented here but are available upon request.

4 Ordinary Least Squares (OLS) and Two Stages Least Squares (2SLS) estimation methods were also used. By comparing, the 2SLS and GMM estimations show quite similar results in terms of coefficients signs and magnitudes while the OLS estimation differs regarding the sign of the competition effect.

5 Enough evidence is found to reject the null hypothesis of the Pagan-Hall general test. The estimates are then presented with standard errors which are robust to arbitrary heteroskedasticity. 6 The classification of industries is shown in Appendix blackA 7 The differences in the coefficient estimates between both industries are statistically significant according to the Chow test. The F statistic of the test is 59.54 and the p-value is 0.000.

8 According to the Population and Housing Census of 2010, urban settlements are defined by provincial and cantonal capitals under the political and administrative division of the country. Rural areas include parish capitals, other towns, peripheries and sparsely populated settlements

A List of industries B Robustness checks

Regarding the test of under-identification presented at the bottom of

C Selection model

The fact that all sectors do not operate in each canton might be a selection issue. Firms' decisions might not be taken randomly. For that reason, it is compelling to deal with the presence of many zeros in the database. To do so, the Heckman's method (Heckman,1979) is applied. It considers a random sample on I observations. Some of them are observed and the others are not observed. If this is a selection process, then: (1a) Y 1i = X 1i β 1 + U 1i (1b) Y 2i = X 2i β 2 + U 2i E(U ji , U j i ) = σ jj , i = i Suppose that observable data on Y 1i exists if Y 2i ≥ 0. Thus, in the general case:

E(U 1i | X 1i , sample selection rule) = E(U 1i | X 1i , Y 2i ≥ 0) = E(U 1i | X 1i , U 2i ≥ -X 2i β 2 )
Assuming that h(U 1i , U 2i ) is a bivariate normal density:

E(U 1i | U 2i ≥ -X 2i β 2 ) = σ 12 (σ 22 ) 1/2 λ i E(U 2i | U 2i ≥ -X 2i β 2 ) = σ 22 (σ 22 ) 1/2 λ i where λ i = φ(Z i ) Φ(-Z i ) , Z i = -X 2i β 2 (σ 22 ) 1/2
and φ and Φ are the density and distribution function for a normal standard variable, respectively. λ i is the inverse of Mill's ratio. It corresponds to the decreasing function of the probability that an observation is selected in the sample.

In order to implement this method, [START_REF] Heckman | Sample selection bias as a specification error[END_REF] suggests to estimate λ i using a probit analysis for the whole sample. In the probit selection model, the dependent variable is a dummy that equals to 1 if a given industry exists in a given canton and 0 if not. According to location theories, the firmsâ location would be guided by factors such as transport costs, the availability of labour, raw materials and agglomeration economies and diseconomies [START_REF] Combes | Estimating agglomeration economies with history, geology, and worker effects[END_REF]. This is the case of this model, the explanatory variables are the level of urbanisation in a given canton, the inverse distance to main cities (Quito, Guayaquil and Cuenca) and industry dummies. Variables such as the cantonal income or variables related to agglomeration economies cannot be introduced in this selection model since they are already taken into account in the original model ( 7) and could produce high multicollinearity. In Table black1, the results of the Probit model show that the probability that an industry operates in a given canton positively depends on the level of urbanisation and the industrial characteristics. The proximity to main cities, Quito in particular, also determines the presence of industries in a canton. The correct prediction rate is 82.62%. Using the resulting estimates, λ i can be computed. Then, such a value is included in the main equation ( 7) that analyses the effects of agglomeration externalities in productivity.

  (a) Firms density by canton (b) Employment's density by canton

Figure 1 :

 1 Figure 1: Density of economic activities in cantons Elaborated by authors based on the Economic Census 2010, Ecuador.

Figure 2 :

 2 Figure 2: Diversity and productivity of cantons Elaborated by authors based on the Economic Census 2010, Ecuador.

Figure 3 :

 3 Figure 3: Canton size vs diversification Elaborated by authors based on the Economic Census 2010, Ecuador.

Figure 4 :

 4 Figure 4: Histogram of urbanisation of cantons in Ecuador Elaborated by authors based on Population and Housing Census of Ecuador, 2010.

Table 1 .

 1 Validity tests of instruments

		Correlation between instruments and endogenous variables Exogeneity test
		Specialisation Diversity	Density firms	C-statistic
	threat index	-0.0813	0.1439	0.1914	1.25
		(0.000)*	(0.000)*	(0.000)*	(0.264)
	squared density	0.2685	0.108	0.5513	0.978
		(0.000)*	(0.000)*	(0.000)*	(0.323)
	Popd 90	-0.1097	0.2902	0.8059	0.042
		(0.000)*	(0.000)*	(0.000)*	(0.838)
	Spe spatial lag	-0.0045	-0.0005	-0.0054
		(0.6895)	(0.9672)	(0.6319)
	Div spatial lag	-0.0101	-0.0261	-0.022	0.074
		(0.3673)	(0.0197)*	(0.0494)*	(0.786)
	Comp spatial lag	-0.0144	-0.0253	-0.0234	0.062
		(0.1990)	(0.0242)*	(0.0366)*	(0.804)
	Den firms spat lag	0.2685	0.108	0.5513	0.923
		(0.000)*	(0.0001)*	(0.000)*	(0.337)

Table 3 .

 3 Estimation of the effect of agglomeration and industry structure on productivity

	Dependent variable	(1)	(2)	(3)
	Total Factor Productivity	GMM total	Manufacturing	Services
	specialisation	-0.126	-0.228	-0.16
		(-6.02)***	(-4.034)***	(-5.334)***
	diversity	0.353	-1.141	0.378
		(8.53)***	(-2.159)**	(7.495)***
	firms density	-0.009	0.0515	-0.00182
		(-0.83)	(1.766)*	(-0.136)
	competition	0.073	0.254	0.0689
		(7.15)***	(5.746)***	(5.599)***
	selection	-0.270	-0.255	-0.052
		(-6.17)***	(-3.950)***	(-1.556)
	Constant	8.551	12.11	8.422
		(71.92)***	(10.511)***	(62.523)***
	N	7803	2198	5605
	F	32.01	9.444	11.1
	Province specific effects	Yes	Yes	Yes
	Industry specific effects	Yes	Yes	Yes
	Instrumented	spe,den firms	den firms, spe, div	den firms, spe
	Included instruments	comp, div, prov i, ind s	comp, prov i, ind s	comp, div, prov i, ind s
	Excluded instruments	menace,den 2 ,pop90	menace, den 2 , pop90	menace, den 2 , pop90
	div spatial compt spatial div spatial, den firms spatial, div spatial, den firms spatial,
		den firms spatial	comp spatial	comp spatial
	Endogeneitytest	265.044 (0.000)	91.79(0.000)	77.41 (0.000)
	K P(Under-id.test)	1036.6 (0.000)	29.71 (0.000)	563.48 (0.000)
	Hansen J statistic	3.532	0.993	3.05
	J p value	0.473	0.803	0.55
	t statistics in parentheses
	* p < .10, ** p < .05, *** p < .01
	All variables are in logarithm except for TFP.

Table 4 .

 4 Agglomeration effects vs. the level of urbanisation (TFP)

	Dependent variable	(1)	(2)	(3)	(4)
	Total Factor Productivity	urb <33%	urb >33%	urb >46%	urb >61%
	specialisation	-0.158	-0.210	-0.188	0.377
		(-4.968)***	(-7.660)***	(-5.359)***	(7.322)***
	diversity	0.106	0.607	1.520	6.585
		(1.487)	(10.686)***	(5.119)***	(8.459)***
	firms density	-0.0301	0.0315	0.0415	-0.456
		(-1.282)	(2.141)**	(1.938)*	(-5.116)***
	competition	0.115	0.0346	-0.0347	-0.239
		(6.399)***	(2.359)**	(-1.454)	(-4.727)***
	selection (Mill's ratio)	0.0207	0.155	0.273	0.658
		(0.507)	(3.317)***	(4.443)***	(5.267)***
	Constant	10.82	8.011	5.153	-9.342
		(10.908)***	(23.766)***	(5.585)***	(-4.202)***
	N	2983	4820	3524	2028
	F	15.88 (0.000) 31.53 (0.000) 21.25 (0.000) 7.705 (0.000)
	Province specific effects	Yes	Yes	Yes	Yes
	Industry ag. specific effects	Yes	Yes	Yes	Yes
	K P Underidentification test 464.75 (0.000) 632.38 (0.000) 81.59 (0.000) 122.37 (0.000)
	Hansen J statistic	3.134	4.26	1.456	1.156
	J p-value	0.536	0.372	0.692	0.764
	t statistics in parentheses
	* p < .10, ** p < .05, *** p < .01

Table 3 ,

 3 the Kleibergen-Paap rk Wald statistic shows that both equations are identified. According to the Hansenâs J test, the instruments are exogenous. The test of Kleibergen-Paap rk Wald F statistic equals to 17.08 is larger than Stock-Yogo weak ID test critical values for K1=endogenous variables and L1=excluded instruments (12.20). It shows that the instruments are not weak at 5% of maximal bias of the IV estimator relative to OLS.According to the Variance Inflation Factor (VIF) presented in Table black??, the explanatory variables do not lead to multicollinearity issues in the model.In order to detect whether the estimated models present spatial correlation, a test of the presence of spatial correlation in the residuals for each industry (78 industries) is implemented. The results are presented in Tables black?? and black??. According to the Moran's I test, the null hypothesis of zero spatial correlation cannot be rejected. Then, the residuals of the estimations do not present spatial autocorrelation.

		Model
		Variable	VIF
	Collinearity diagnostics	competition specialisation 1.07 1.82
		diversity	1.28
		firms density 1.76
		Measure of global spatial correlation
	ISIC two digit Sectors	I	E(I) sd(I)	z
	p-value
	C10	-0.006	-0.005 0.983 -0.002 0.499
	C11	0.251	-0.016 0.152 1.751 0.04
	C12	-0.775		-0.5 0.099 -0.725 0.234
	C13	0.168	-0.011 0.067 2.643 0.004
	C14	-0.471	-0.005 0.973 -0.479 0.316
	C15	0.104	-0.012 0.072 1.611 0.054
	C16	-0.17	-0.005 0.985 -0.167 0.433
	C17	-0.084	-0.037 0.074 -0.628 0.265
	C18	0.051		-0.01 0.065 0.931 0.176
	C19	-0.131	-0.143 0.364 0.032 0.487
	C20	0.01	-0.026 0.286 0.126 0.45
	C21	0.627	-0.167 0.405 1.959 0.025
	C22	0.096		-0.02 0.195 0.596 0.275
	C23	-0.018	-0.007 0.982 -0.011 0.496
	C24	0.204	-0.029 0.966 0.241 0.405
	C25	-0.076	-0.005 0.988 -0.072 0.471
	C26	0.108	-0.071 0.112 1.605 0.054
	C27	-0.019	-0.042 0.068 0.334 0.369
	C28	-0.076	-0.022 0.201 -0.27 0.394
	C29	0.029	-0.016 0.122 0.371 0.355
	C30	-0.03	-0.053 0.111 0.203 0.419
	C31	0.016	-0.005 0.986 0.022 0.491
	C32	0.053		-0.01 0.063 0.99 0.161
	C33	0.003	-0.009 0.05 0.229 0.409
	D35	0.017	-0.009 0.986 0.022 0.491
	E36	-0.146	-0.014 0.102 -1.298 0.097
	E37	-0.146	-0.014 0.102 -1.298 0.097
	E38	-0.194	-0.036 0.042 -3.722 0.16
	E39	-0.237	-0.167 0.112 -0.624 0.266
	F41	0.07	-0.018 0.035 2.508 0.006
	F42	0.046	-0.023 0.243 0.286 0.387
	F43	0.131	-0.016 0.127 1.155 0.124
	G45	-2.265	-0.005 0.986 -2.292 0.011
	G46	-0.251	-0.006 0.988 -0.249 0.402
	G47	0.138	-0.005 0.988 0.144 0.443
	H49	0.035	-0.006 0.034 1.099 0.136
	H50	-0.172		-0.1 0.099 -0.725 0.234
	H51	0.079	-0.037 0.067 1.743 0.041

Table 1 :

 1 Probit model, presence of zeros

		(1)
		Probit
	urban rate	2.39
		(39.46)***
	Quito	5.07
		(6.02) ***
	Guayaquil	1.15
		(0.88)
	Cuenca	-0.61
		(-1.51)
	industry dummies significant
	Constant	-2.97
		(-17.29)***
	Observations	17238

z statistics in parentheses * p < .05, ** p < .01, *** p < .001

Results

Prior to discussing the results of the estimates, the endogeneity test (Sargan-Hansen statistic) of the variables of agglomeration externalities is presented in Table 2. According to the results, the null hypothesis that industrial concentration and firms' density are exogenous is rejected. They are therefore treated as endogenous variables. Diversity is treated according to the exogeneity test and competition is exogenous and is treated as such. The robustness checks (shown at the bottom of Table 3) of homoscedasticity 5 , over-identification, weakness of instruments, spatial correlation and colinearity (see Appendix blackB) verify that estimations are reliable. Potential selection bias in the sample is also controlled for by introducing the inverse Mill's ratio, computed using the estimates from a Probit model (see Appendix blackC). It is worth noting that since there are more instrumental variables than endogenous variables (Z > X), the GMM estimator is appropriate and more efficient than the 2SLS estimator.

Sectoral composition, density and local productivity

Table 3 shows the estimation results. Column (1) records the estimates of the model using the whole sample. These regression results do not account for industrial heterogeneity. Sectors might be very different in terms of productivity dynamics and backward and forward linkages. A distinction is then made between manufacturing (column 2) and service 6 activities (column 3) 7 .

At global level, productivity gains arising from sectoral diversity are important and significant on the whole sample. This result is in accordance with other studies focusing on Brazil and Chile which show that diversity is the most important driver of productivity (blackDa Silva Catela et al., black2010;blackSaito and Gopinath, black2009). In particular, the positive effect is present in the the service industry. Indeed, the existence of a large variety of activities is often beneficial for the emergence and development of many service industries both for supply and demand linkages and the collaborative environment it offers.

The tertiary sector seems to be ongoing in this economy, encouraged by agglomeration economies (blackMeliciani and Savona, black2014


Héctor Delgado
email: delgado@eurecom.fr

Massimiliano Todisco
email: todisco@eurecom.fr

Md Sahidullah
email: sahidullahmd@gmail.com

Nicholas Evans
email: evans@eurecom.fr

Tomi Kinnunen

Aik Kong

Junichi Lee

Yamagishi
email: jyamagis@nii.ac.jp

Kong Aik Lee
email: k-lee@ax.jp.nec.com

Junichi Yamagishi

meta-data analysis and baseline enhancements

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Automatic speaker verification (ASV) [START_REF] Kinnunen | An overview of text-independent speaker recognition: from features to supervectors[END_REF][START_REF] Hansen | Speaker recognition by machines and humans: A tutorial review[END_REF] has matured considerably over the last few decades. As an efficient, convenient, low-cost and reliable solution to person authentication, ASV technology is finding its way into a growing array of commercial products and services. The research community has also reacted to concerns regarding the vulnerability of ASV technology to spoofing [START_REF] Evans | Spoofing and countermeasures for automatic speaker verification[END_REF], also know as presentation attacks [START_REF]Information technology -biometric presentation attack detection[END_REF], namely attempts by fraudsters to interfere with the normal operation of an ASV system using specially crafted speech signals.

Vulnerabilities to spoofing are clearly unacceptable; if not addressed, they stand to dent confidence and jeopardize the commercial exploitation of ASV technology.

So-called spoofing countermeasures, also known as presentation attack detection (PAD) systems, are typically auxiliary systems which operating along side an ASV system in order to detect and deflect spoofing attacks. The Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) challenge series was born in order to spearhead their development. Two such challenges have been held to date. The first focused on developing countermeasures to defend against converted voice and synthetic speech spoofing attacks whereas the second focused on replay attacks. Both challenges were accompanied by a standard database, protocol and metric meaning that, for the first time, the performance of competing countermeasure solutions could be meaningfully compared. The databases for both challenges have been released into the public domain 1,2 .

Subsequent to the release of the ASVspoof 2017 database, the organisers became aware of a number of data anomalies. These were patched in a second version of the database released in February 2018 3 . This paper describes Version 2 of the ASVspoof 2017 database in addition to a set of newly released meta-data, which describes the playback and recording devices in addition to the different acoustic environments used in its collection. The paper presents an in-depth analysis of replay detection performance using the new meta-data.

Also new to this paper are a number of enhancements to the original baseline system based upon a constant Q cepstral coefficient (CQCC) [START_REF] Todisco | A new feature for automatic speaker verification anti-spoofing: Constant Q cepstral coefficients[END_REF][START_REF] Todisco | Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification[END_REF] frontend and Gaussian mixture model backend. The paper compares results obtained for both Version 1.0 and Version 2.0 of the ASVspoof 2017 database and both the original and enhanced baseline systems. Enhancements comprise log-energy coefficients, cepstral mean and variance normalisation and an alternative backend i-vector classifier.

The paper is organised as follows. Section 2 describes the original ASVspoof 2017 database and the differences introduced in Version 2.0. A description of the replay data collection procedures and meta-data analysis is presented in Section 3. Baseline enhancements are presented in Section 4. Results and conclusions are presented in Sections 5 and 6 respectively.

ASVspoof 2017 database

The ASVspoof 2017 database was collected in order to foster the development of countermeasures to protect ASV systems from replay spoofing attacks. This section provides a brief overview of the data collection process and data partitions. These are the same as for the original Version 1.0 database that was used for the 2017 challenge. Also described here are changes introduced in Version 2.0.

Data collection

The ASVspoof 2017 corpus is a collection of bona fide and spoofed utterances. Bona fide utterances are a sub-set of the RedDots corpus4 [START_REF] Lee | The RedDots data collection for speaker recognition[END_REF] collected and released previously in support of research in text-dependent ASV. The RedDots database was collected by volunteers using Android smart phones. Utterances correspond to one of the ten different, fixed pass-phrases (see [START_REF] Kinnunen | Reddots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research[END_REF] for more details). Spoofed utterances are the result of replaying and recording bona fide utterances using a variety of heterogeneous devices and acoustic environments. The later are intended to simulate replay spoofing attacks [START_REF] Wu | A study on replay attack and anti-spoofing for text-dependent speaker verification[END_REF][START_REF] Alegre | Re-assessing the threat of replay spoofing attacks against automatic speaker verification[END_REF]. A total of 57% of replay utterances were collected by four participants of the EU Horizon 2020-funded OCTAVE project 5 (see [START_REF] Kinnunen | Reddots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research[END_REF]) while the remaining 43% were collected by other contributors.

Data partitions

The ASVspoof 2017 corpus is partitioned into three subsets: training, development and evaluation. A summary of their composition is presented in Table 1. Data corresponds to 177 different replay sessions and 61 distinct replay configurations. The number of replay configurations which comprise the evaluation subset is considerably larger than that which comprise the training and development subsets. Meta-data consisting of bona fide/spoofed (nonreplay/replay) ground-truth labels, in addition to speaker IDs, phrase IDs, and replay configuration details were provided to ASVspoof 2017 challenge participants for the training and development subsets only. While corresponding details for the evaluation set was withheld originally, it has since been released publicly with Version 2.0 of the ASVspoof 2017 data. It supports the deeper analysis of countermeasure performance (e.g. per acoustic environment, playback device or recording device).

Database update

Post-evaluation, the organisers became aware6 of a number of data anomalies that have potential to influence results and find-Figure 1: An illustration of the replay spoofing scenario. Figure adapted from [START_REF] Kinnunen | Reddots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research[END_REF].

ings [START_REF] Chettri | A deeper look at Gaussian mixture model based anti-spoofing systems[END_REF]. These mostly involve periods of silence, or zerovalued samples that are present in the original RedDots data [START_REF] Lee | The RedDots data collection for speaker recognition[END_REF]. While being small in number and while being of little consequence to the use of RedDots data for ASV research, these differences can impact on the assessment of replay detection performance. The same zero-valued samples are distorted to nonzero values as a result of playback, propagation in an acoustic environment and re-recording and such stark differences (zero/non-zero values) are easily detected. While the number of affected trials and duration of zero-valued samples was not substantial, these anomalies may yet bear influence on replay detection results especially, e.g., for approaches which exploit some form of temporal attention mechanism. Accordingly, these data anomalies have been corrected through the release of the ASVspoof 2017 Version 2.0 database. All results presented in this paper relate to this updated version.

Replay meta-data

This section introduces the meta-data that accompanies the release of ASVspoof 2017 Version 2.0. Described here is the general approach to replay simulation and the different playback, recording devices and acoustic environments used in database collection. Their different combinations lead to 61 distinct replay configurations.

Replay simulation

Practical replay spoofing attacks are assumed to be implemented according to the diagram illustrated in Fig. 1. Replay attacks comprise recordings of a bona fide access attempt that are subsequently replayed or represented to the microphone of the ASV system.

As described in [START_REF] Kinnunen | Reddots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research[END_REF][START_REF] Kinnunen | The asvspoof 2017 challenge: Assessing the limits of replay spoofing attack detection[END_REF], the ASVspoof 2017 evaluation considered a worst case scenario where attackers have a digital copy of bona fide recordings, and specifically, a test utterance of the target speaker. These are then replayed and recaptured according to the scenario illustrated in Fig. 1. Accordingly, the acoustic impacts upon an utterance encompass those of a playback device, a recording device and an acoustic environment through which sound propagates. A combination of these is referred to a replay configuration (RC).

With the objective of measuring the limits of replay attack detection, the ASVspoof 2017 database was designed to contain a diverse range of replay configurations including conditions for which the detection of replay attacks should be relatively straightforward, to those for which detection should be considerably challenging.

UK), and their colleagues for bringing the problem to our attention. It is stressed that the categorisations of playback and recording devices represented below reflect qualitative indicators (e.g. based on device size) rather than scientific or quantitative measures of quality. They should be interpreted accordingly.

Acoustic environments

The acoustic environment is the physical space in which original speech data is replayed and re-recorded. The ASVspoof 2017 database was collected in a total of 26 different environments, each listed in Table 2 (Environment) and denoted E01-E26. Variations between them include types and levels of additive ambient and convolutive reverberation noise. The level of noise is assumed to be inversely correlated with the threat to ASV posed by replay attacks recorded in each environment. Colour codes in Table 2 serve as an indicator of the replay threat: low (green), medium (yellow) and high (red).

The two balcony and one cantine conditions are high ambient noise environments, e.g. traffic noise or background chatter, where corresponding replay recordings are assumed to pose little threat to ASV (high noise should be detected with relative ease). The eight different home conditions include recordings made in living rooms and bedrooms characterised by medium ambient noise levels. The ten office conditions also contain medium ambient noise, generally produced by air conditioning systems, but also exhibit reverberation.

There are four low noise conditions. Anechoic room recordings exhibit very low additive noise and reverberation whereas studio recordings contain similarly low levels of ambient noise, but also a degree of reverberation. Analogue wire conditions simulate recordings made without physical sound propagation, but with electrical propagation from a playback device directly to an ASV system. Replay recordings made in these condition are assumed to present the stiffest challenge to replay attack detectors.

Table 3: As for Table 2 except for playback devices. Recordings made with high quality playback devices are assumed to be the most difficult to detect (red). Those collected with lower quality devices are assumed to be detected with relative ease (green). Other devices are assumed to be of medium quality (yellow).

ID Playback device P01

All

Playback devices

There are 26 playback devices, denoted P01-P26, as listed in Table 3. Replay samples collected from consumer-grade portable replay devices, e.g. smart phones, laptops and tablets equipped with in-built, generally small loudspeakers, are assumed to be of lower quality and should be detected with relative ease (green). Replay samples collected using consumer devices with larger loudspeakers, e.g. desktop PCs with external loudspeakers, are assumed to be of medium quality (yellow); they are assumed to introduce less acoustic distortion than smaller, in-built loudspeakers. Professional audio equipment such as active studio monitors and studio headphones are assumed to result in replay samples of comparatively high quality; they are assumed to pose the gravest threat (red). Also included among high quality devices are audio interfaces or analogue outputs used in the collection of recordings in analogue wire environments.

Recording devices

There are 25 recording devices. They are illustrated in Table 4, denoted R01-R25. Replay samples collected from mobile or battery powered devices with in-built, miniature microphones, e.g. smart phones and laptops are assumed to be of lower quality. Sample collected from such devices are assumed to be detected with ease (green). Samples collected from studio-quality condenser microphones or hand-held recorders are assumed to be of higher quality. Such recording are assumed to present a graver threat (red). Also included among high-quality devices are audio interfaces or analogue inputs used in the collection of recordings in analogue wire attacks. Powered desktop devices such as headsets or webcams, are assume to give recordings of medium quality (yellow).

Table 4: As for Tables 2 and3 except for recording devices. Recordings made with high quality devices are assumed to be the most difficult to detect (red). Those collected with lower quality devices are assumed to be detected with relative ease (green). Other devices are assumed to be of medium quality (yellow).

ID

Replay configurations

The ASVspoof 2017 database contains recordings collected with diverse replay configurations (RCs). According to the schematic illustrated in Fig. 1, each RC comprises one playback device, one acoustic environment and one recording device. More formally, a RC is defined as a triplet RCc = (Ei, Pj, R k ) where c enumerates all unique triplets (i, j, k), i indexing environments, j playback devices and k recording devices. Only a subset (61) of the 26×26×25 = 16, 900 possible RC combinations are represented; not all devices/environments were available to all the crowd-sourcing data collectors.

In order to aid analysis, the number of distinct RCs reported previously [START_REF] Kinnunen | The asvspoof 2017 challenge: Assessing the limits of replay spoofing attack detection[END_REF] was reduced by the grouping together of overlapping configurations. Version 2 of the ASVspoof 2017 database contains 61 distinct RCs as indicated in Table 1.

As a precursor to further analysis, the set of replay recordings were ranked in terms of the threat they present to ASV. The reasoning for such analysis is to compare subsequently the correlation between the supposed threat and spoofing detection performance. Replay attacks which pose the least threat to ASV are assumed to be of poor quality (high noise or distortion) as compared to bona fide speech. It is a reasonable assumption that these replay attacks should also be detected by a spoofing countermeasure with relative ease. In contrast, high quality replay recordings are expected to pose a greater threat to ASV and also be more challenging to detect since they are potentially more similar to bona fide speech.

A standard Gaussian mixture model with universal background model (GMM-UBM) ASV system was used for ranking [START_REF] Delgado | Further optimisations of constant Q cepstral processing for integrated utterance verification and text-dependent speaker verification[END_REF]. It uses a Mel-frequency cepstral coefficient (MFCC) front-end and a 512-component UBM trained using RSR2015 [START_REF] Larcher | Textdependent speaker verification: Classifiers, databases and RSR2015[END_REF] and TIMIT 7 databases. Phrase-dependent target speaker models are created from RedDots enrolment data. Under a spoofing-free scenario, this system achieves an equal error rate (EER) of 1.8% on the evaluation set. The EER for each RC is computed using all replay segments recorded with the given RC and the corresponding bona fide counterparts.

Fig. 2 (blue solid profile) shows a sorted rank of the EER obtained for each of the 57 RCs corresponding to the evaluation set. Without exception, replay attacks collected in all RCs succeed in spoofing the ASV system to some extent; all EERs are above those for the spoofing-free scenario. Also evident from results presented in Fig. 2 is a substantial variation in EER across different RCs (from 2.0% to 48.0%).

Table 5 shows a list of the same 57 distinct RCs of the evaluation set sorted according to the EER ranking in Fig. 2. In addition to the number of evaluation segments corresponding to each RC, also illustrated in Table 5 is the number of distinct playback devices (P), acoustic environments (E) and recording devices (R) used for their collection. Colours reflect the same qualitative indicators of Tables 2-4. Higher quality replay attacks (higher-numbered RCs) that are assumed to present a greater challenge to replay spoofing countermeasures are indicated in red (high quality devices and benign acoustic environments). Lower quality attacks (lower quality devices and harsh acoustic environments) that are assumed to be detected with relative ease are indicated in green.

Noisy acoustic environments appear exclusively to the left in Table 5. With only one exception, RCs with high quality playback and recording devices appear to the right. As might be expected, analogue wire attacks (RCs 55-57) are among the most harmful to ASV, producing near random decisions (Fig. 2), and will certainly be among the most difficult to detect. Of course the general trend is more complex. The three components that comprise the RC do not have the same influence on ASV threat and are unlikely to have the same influence of detection difficulty, e.g. the quality of a playback or recording device is of little influence when the acoustic environment is harsh. As a consequence, strict and consistent trends are difficult to observe in practice.

Baseline countermeasure enhancements

New to the 2017 edition of the ASVspoof evaluation was the introduction of a baseline countermeasure. It is based upon a constant Q cepstral coefficient (CQCC) frontend [START_REF] Todisco | A new feature for automatic speaker verification anti-spoofing: Constant Q cepstral coefficients[END_REF][START_REF] Todisco | Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification[END_REF] and standard Gaussian mixture model (GMM) backend classifier. This section describes the original ASVspoof 2017 baseline configuration and enhancements which deliver improved spoofing detection performance for replay attacks, specifically log-energy coefficients and cepstral mean and variance normalisation, in addition to an alternative i-vector backend classifier.

CQCC features

CQCC features are derived using the constant Q transform (CQT) [START_REF] Brown | Calculation of a constant Q spectral transform[END_REF][START_REF] Schörkhuber | A matlab toolbox for efficient perfect reconstruction time-frequency transforms with log-frequency resolution[END_REF], a perceptually motivated time-frequency analysis tool and alternative to the short-term Fourier transform (STFT). Whereas the STFT operates with a fixed spectrotemporal resolution, that of the CQT is variable, with a higher frequency resolution at lower frequencies and a higher temporal resolution at higher frequencies. Just like conventional Melfrequency cepstral coefficients, CQCC extraction is performed

R C 1 R C 2 R C 3 R C 4 R C 5 R C 6 R C 7 R C 8 R C 9 R C 1 0 R C 1 1 R C 1 2 R C 1 3 R C 1 4 R C 1 5 R C 1 6 R C 1 7 R C 1 8 R C 1 9 R C 2 0 R C 2 1 R C 2 2 R C 2 3 R C 2 4 R C 2 5 R C 2 6 R C 2 7 R C 2 8 R C 2 9 R C 3 0 R C 3 1 R C 3 2 R C 3 3 R C 3 4 R C 3 5 R C 3 6 R C 3 7 R C 3 8 R C 3 9 R C 4 0 R C 4 1 R C 4 2 R C 4 3 R C 4 4 R C 4 5 R C 4 6 R C 4 7 R C 4 8 R C 4 9 R C 5 0 R C 5 1 R C 5 2 R C 5 3 R C 5 4 R C 5 5 R C 5 6 R C 5 7
Equal error rate (EER, in %) Table 5: A list of replay configurations sorted according to the ASV EER ranking in Figure 2. Colour codes reflect the supposed threat to ASV (Tables 2 to 4). E denotes acoustic environment, P denotes playback device, R denotes recording device. Numbers reflect distinct replay sessions and number of segments. with a filterbank, where the Q factor is a measure of the selectivity of each filter, defined as the ratio between the centre frequency of the filter and its bandwidth. Cepstral analysis cannot be applied directly to the CQT since frequency bins are on a different scale to those of the basis functions of the discrete cosine transform; they are respectively geometrically and linearly spaced. This problem is solved by converting geometric space to linear space via re-sampling [START_REF] Todisco | Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification[END_REF] before otherwise conventional cepstral analysis is applied.

Designed initially for the detection of voice conversion and speech synthesis spoofing attacks, the CQCC frontend was applied to the detection of replay spoofing attacks with only minor modifications.The CQT is applied with a maximum frequency of Fmax = FNY Q, where FNY Q is the Nyquist frequency of 8kHz. The minimum frequency is set to Fmin = Fmax/2 9 15Hz (with 9 being the number of octaves). The number of bins per octave B is set to 96. Re-sampling is applied with a sampling period of 16 bins in the first octave. Resulting feature vectors are of dimension 19, excluding the C0 coefficient (cf. 29 coefficients + C0 for the original system). Full details of the CQCC extraction procedure are reported in [START_REF] Todisco | A new feature for automatic speaker verification anti-spoofing: Constant Q cepstral coefficients[END_REF][START_REF] Todisco | Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification[END_REF].

Log-energy coefficients

While frame-level energy coefficients are used extensively in a multitude of different speech-related tasks [START_REF] Young | The htk book[END_REF][START_REF] Dehak | Front-end factor analysis for speaker verification[END_REF][START_REF] Saeidi | I4U submission to NIST SRE 2012: A large-scale collaborative effort for noiserobust speaker verification[END_REF], they have not been applied extensively to spoofing detection problem in the context of the ASVspoof challenge. Even if the absolute energy of both bona fide and replayed speech signals may reflect more the gain of a recording device, rather than acting as an strict indicator of spoofing, it is hypothesised here that nonlinear changes to energy dynamics may serve as an indication of replay. Accordingly, the use of log-energy parameters for spoofing detection has been explored.

Although the log-energy can be calculated from the timedomain signal, log-energy coefficients are here calculated in the CQT domain in order to preserve the time resolution of CQCC coefficients. Parseval's theorem states that the total energy computed in the time domain is equal to the total energy computed in the frequency domain. In the frequency domain, the log-energy is defined as the logarithm of the summation of the spectral components, normalised by the sample size. It can be calculated according to:

log E(n) = log K k=1 X CQ (k, n) 2 -log(K) (1)
where n is the frame index, k = 1, 2, ..., K is the frequency bin index and where X CQ (k, n) is the frame-blocked CQT [START_REF] Todisco | A new feature for automatic speaker verification anti-spoofing: Constant Q cepstral coefficients[END_REF][START_REF] Todisco | Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification[END_REF].

Cepstral mean and variance normalisation

Cepstral mean and variance normalization (CMVN) [START_REF] Auckenthaler | Score normalization for text-independent speaker verification systems[END_REF] is an efficient normalisation technique used to remove nuisance channel effects which might otherwise degrade the performance of a variety of different approaches to automatic speech and speaker recognition. Other researchers have reported the benefit of using CMVN for spoofing detection, e.g. [START_REF] Font | Experimental analysis of features for replay attack detection results on the ASVspoof 2017 challenge[END_REF][START_REF] Lavrentyeva | Audio replay attack detection with deep learning frameworks[END_REF][START_REF] Ji | Ensemble learning for countermeasure of audio replay spoofing attack in asvspoof2017[END_REF][START_REF] Nagarsheth | Replay attack detection using dnn for channel discrimination[END_REF], all of which relate to replay detection within the scope of ASVspoof 2017.

The application of CMVN to replay spoofing detection may at first seem counter-intuitive. The playback and recording of speech in different acoustic environments using different devices is akin to the accumulation of additional channel effects. CMVN, which aims to attenuate channel effects, may then be to the detriment of replay detection. This assumption may only hold, however, if bona fide speech were to be captured across a common, consistent channel. This is not the case for the ASVspoof 2017 source data, namely the RedDots database, which was captured using heterogeneous devices and channels [START_REF] Lee | The RedDots data collection for speaker recognition[END_REF].

Accordingly, CMVN may help to align both bona fide and replayed speech distributions to a common scale, and hence force spoofing detection to discriminate between the two according to influences other than those caused by channel differences (which are present in both). The use of CMVN is also expected to improve the reliability of spoofing detection across the diverse variation in replay attacks which characterises the ASVspoof 2017 database.

Backend classifier

The original baseline system uses a GMM backend classifier. We have also investigated the performance of an alternative ivector approach [START_REF] Dehak | Front-end factor analysis for speaker verification[END_REF]. Both are described below.

The GMM backend is the same as the original baseline. It uses models of 512 components. Different models are learned for bona fide and spoofed speech with an expectationmaximisation (EM) algorithm with random initialisation. Classifier scores for a given test utterance are computed as the loglikelihood ratio Λ(X) = log L(X|θn) -log L(X|θs), where X is a sequence of CQCC feature vectors, L denotes the likelihood function, and θn and θs represent the GMMs for bona fide and spoofed speech, respectively.

The alternative i-vector backend [START_REF] Dehak | Front-end factor analysis for speaker verification[END_REF] uses a universal background model of 64 components which is trained on the ASVspoof 2017 training partition. The total variability space matrix T has 100 factors and is learned using the same data. The i-vectors are mean normalised, whitened and treated with within-class covariance normalization (WCCN). The i-vector training data are averaged before length-normalization. Single i-vectors are learned for bona fide and spoofed speech classes. The proposed i-vector backend also uses the same CQCC frontend as the GMM-based classifier.

Results and analysis

Several sets of experimental results are reported. The first shows differences in performance for original baseline systems for versions 1.0 and 2.0 of the ASVspoof 2017 database. The second set of experiments assess improvements to performance delivered by baseline enhancements. Last, we analyse replay spoofing detection nuances according to different replay configurations.

ASVspoof 2017 Version 2.0

Fig. 3 illustrates replay detection performance in terms of EER for versions 1.0 and 2.0 of the ASVspoof 2017 database and for original baseline systems B01 and B02 [START_REF] Kinnunen | The asvspoof 2017 challenge: Assessing the limits of replay spoofing attack detection[END_REF]. Also included are 95% confidence intervals calculated using the methodology explained in [START_REF] Bengio | A statistical significance test for person authentication[END_REF], and using the specific configuration described in [START_REF] Sholokhov | Semisupervised speech activity detection with an application to automatic speaker verification[END_REF]. Differences between systems B01 and B02 lie only in the use of training (T) or pooled training and development data (T+D); the systems are otherwise the same. Differences in performance are minor: 25% to 23% EER for B01 and 31% to 30% for B02. Confidence intervals are highly overlapped, suggesting that the variations in performance are not significant. While the same may not hold for alternative approaches to spoofing detection, these results show that in the case of the particular baseline system, performance is reasonably stable across the two database versions.

Baseline enhancements

Presented here are experiments which assess the benefit of baseline enhancements presented in Section 4, namely the use of log-energy, CMVN and the alternative i-vector backend. Results are presented in Table 6 for different training and testing configurations involving some combination of the default training (T), development (D) and evaluation (E) partitions. Such exhaustive experiments were performed in order to assess the stability of findings across different training and testing conditions.

Results without CMVN appear to the left of Table 6. Those with CMVN appear to the right. Results obtained with 19th order CQCCs and static, delta and acceleration coefficients are denoted by 19-SDA. Those obtained with appended log-energy are denoted by 19E-SDA. Without CMVN normalisation, the use of log-energy decreases performance. Also, the i-vector backend outperforms the GMM backend.

With CMVN, however, the picture is quite different. Results to the right of Table 6 show that the use of log-energy leads to better performance. There is a substantial improvement

Meta-data analysis

Fig. 2 also shows an analysis of performance per RC for the GMM and i-vector backend classifiers. GMM system uses the CQCC 19E-SDA configuration with CMVN, while the i-vector system uses the CQCC 19-SDA configuration. The first interpretation of these results is that there is little correlation between the supposed difficulty of detection, as indicated by the EER profile described in Section 3.5, and replay detection performance. This finding reflects the combined, complex effects of the playback device, the acoustic environment and the recording device. As for CM performance, the GMM system outperforms the i-vector system for most RCs.

Table 7 illustrates a decomposition of results in terms of the qualitative indicators displayed in Tables 2 to 4. Results show the impact of a single element of the RC in terms of EER for all bona fide trials versus all pooled replay trials corresponding to the given qualitative category. While not strictly true, variation from other elements of the RC are at least somewhat marginalised. These results show more consistent trends. The impact of the acoustic environment dominates the effect of the playback and recording devices; the trend for playback devices are inconsistent and show little variation, whereas EERs for recording device variation are universally lower but show a consistent trend.

The observations would suggest that, perhaps unsurprisingly, ambient and reverberation noise are reliable indicators of replay spoofing attacks. Replay recordings made in benign acoustic conditions are more difficult to detect; the highest EER in Table 7 is for high quality acoustic environments. They also suggest that the quality of the recording device plays a more significant role in the difficulty of replay detection than the quality of the playback device. These observations would seem to corroborate the trends shown in Table 7: benign acoustic conditions and high quality recording devices are positioned largely to the right (red colours).

Needless today, however, coupled with the results illustrated in Fig. 2, the physical effects upon a speech signal of playback and recording are complex. An understanding of how the variation in replay configurations impacts on different detection systems, and hence exactly what information these systems are using, remains an issue requiring further investigation.

Conclusions

This paper describes the differences between Versions 1.0 and 2.0 of the ASVspoof 2017 database of bona fide and replay spoofing attack recordings. New to this paper is (i) a set of meta-data which describes the diversity of replay configurations used for database collection and (ii) enhancements to the official baseline countermeasure. The latter include the addition of log-energy coefficients and cepstral mean and variance normalisation (CMVN). Despite being somewhat counter-intuitive in terms of replay detection, these enhancements bring a relative reduction to the spoofing detection equal error rate of almost 50%.

Meta-data analysis confirms that the effects of replay spoofing are complex and difficult to interpret. Results suggest that the effect of the acoustic environment is the most influential upon the performance of replay spoofing detection and show that that of the playback device is less important. Different approaches to replay spoofing detection also show substantial variation in performance across different replay configurations, suggesting that they are using different cues to detect replay.

These findings show that the problem of replay spoofing detection remains poorly understood and might suggest that future studies should consider evaluation under controlled conditions. These may allow the influence of each component in a replay attack to be isolated and studied more reliably. Findings from such studies may then help to design more reliable replay spoofing countermeasures. The findings from the study reported in this paper will feed into the roadmap for future ASVspoof evaluations.

Figure 2 :

 2 Figure2: Impact of different replay configurations (RCs) upon ASV performance (solid blue profile) measured in terms of equal error rate (%) when a set of zero-effort impostor trials are replaced with replay spoofing trials generated with each RC. Also illustrated is the detection performance of GMM (blue bars) and i-vector (red bars) spoofing countermeasures (CMs) for the same RCs, also expressed in terms of equal error rate (%).

Figure 3 :

 3 Figure 3: Comparison of replay detection performance in terms of equal error rate (with 95% confidence intervals) for the original baseline system for the evaluation set for versions 1.0 and 2.0 of the ASVspoof 2017 database.

Table 1 :

 1 Updated statistics of the ASVspoof 2017 2.0 corpus.

	Subset	# Spk	# Replay sessions	# Replay Config	#Utterances Bona fide Replay
	Training	10	6	3	1507	1507
	Devel.	8	10	10	760	950
	Eval.	24	161	57	1298	12008
	Total	42	177	61	3565	14465

Table 2 :

 2 Acoustic environments used in the collection of the ASVspoof 2017 database. Replay recordings made in high noise conditions (noisier than bona fide speech) are assumed to present a low threat (green) to ASV; they should be detected by replay spoofing countermeasures with relative ease Recordings made in low noise conditions are assumed to pose a graver threat (red); they are like to be difficult to detect. Other recordings are made in medium noise conditions (yellow).

	ID	Environment	ID	Environment
	E01	Anechoic room	E14	Office 02
	E02	Balcony 01	E15	Office 03
	E03	Balcony 02	E16	Office 04
	E04	Home 07	E17	Office 05
	E05	Home 08	E18	Office 06
	E06	Cantine	E19	Office 07
	E07	Home 01	E20	Office 08
	E08	Home 02	E21	Office 09
	E09	Home 03	E22	Office 10
	E10	Home 04	E23	Studio
	E11	Home 05	E24	Analog wire 01
	E12	Home 06	E25	Analog wire 02
	E13	Office 01	E26	Analog wire 03

Table 6 :

 6 Replay detection performance in terms of EER for the ASVspoof 2017 Version 2.0 database, several training and testing configurations, different frontend with and without log-energy coefficients and CMVN normalisation and for GMM and i-vector backends.

		training on	T	D	T	D	T+D	T	D	T	D	T+D
		testing on	D	T		E		D	T		E
		Feat config.		no normalisation				CMVN
	GMM	19-SDA 19E-SDA	11.69 1.36 30.79 25.33 23.97 10.37 1.37 34.95 26.3 29.31	13.31 8.49 19.74 16.89 15.33 9.06 5.64 13.74 14.77 12.24
	i-vector	19-SDA 19E-SDA	4.43 5.11	1.23 17.82 18.81 18.60 1.54 21.47 16.25 21.10	11.61 8.74 16.61 15.08 15.63 10.52 7.27 14.76 14.37 12.93

Table 7 :

 7 Replay detection performance in terms of equal error rate (EER, %) for different qualities of environments and playback and recording devices.

		Low	Medium High
	E: acoustic environment 16.68	18.73	21.86
	P: playback device	16.64	16.44	18.37
	R: recording device	10.80	15.69	17.77
	in terms of EER for both GMM and i-vector systems. These
	observations support the hypotheses outlined in Sections 4.2
	and 4.3. CMVN normalises the log-energy distributions to have
	zero-mean and unit-variance, but retains utterance-level energy
	dynamics which serve as an indicator of spoofing. With the
	application of CMVN, the best performance is achieved using
	19E-SDA CQCC features and a GMM backend. Compared to
	the previous baseline score of 24.0% the best enhanced baseline
	result of 12.2% corresponds to a relative improvement of almost
	50%.
	Also of interest here is the stability of results and trends
	across the different training and testing configurations. Whereas
	results without CMVN show substantial variation for different
	training configurations, each containing different numbers of
	RCs, those with CMVN are more stable. These findings ap-
	pear to confirm the hypothesis that CMVN helps to improve
	generalisation in spoofing detection.

http://dx.doi.org/10.7488/ds/298

http://dx.doi.org/10.7488/ds/2105

http://dx.doi.org/10.7488/ds/2301

https://sites.google.com/site/ thereddotsproject/

https://www.octave-project.eu/

We kindly acknowledge Ricardo P. V. Violato (CPqD lab, Brazil), Pavel Korshunov (IDIAP, Switzerland), and Bhusan Chettri (QMUL,