François Leconte

Julien Bouyer
email: julien.bouyer@cerema.fr

Rémy Claverie
email: remy.claverie@cerema.fr

Mathieu Pétrissans
email: mathieu.petrissans@univ-lorraine.fr

Francois Leconte
email: francois.leconte@cerema.fr

Using Local Climate Zone scheme for UHI assessment: Evaluation of the method using mobile measurements

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

According to climate change scenarios, European cities are likely to experience more intense and more frequent heat waves in the twenty first century [START_REF] Christensen | Regional climate projection[END_REF]. In urban context, heat waves can have significant implications on public health [START_REF] Koppe | Heat-waves: risks and responses[END_REF], energy consumption [START_REF] Demunck | How much can air conditioning increase air temperatures for a city like paris, france?[END_REF] and outdoor thermal comfort [START_REF] Alexandri | Temperature decreases in an urban canyon due to green walls and green roofs in diverse climates[END_REF]. Consequently, the integration of future climate variability to urban planning strategies becomes a raising challenge for the next decades (Alcoforado and Matzarakis, 2010) [START_REF] Grimmond | Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective)[END_REF].

Heat waves periods tend to reinforce the Urban Heat Island (UHI) phenomenon [START_REF] Cantat | L'îlot de chaleur urbain parisien selon les types de temps[END_REF] [START_REF] Tan | The urban heat island and its impact on heat waves and human health in shanghai[END_REF]. The amplitude of the UHI is usually expressed as the synchronous screen-height air temperature difference between urban and rural thermal sensors [START_REF] Stewart | Local climate zones for urban temperature studies[END_REF]. Recognized causes of UHI are the decrease of wind speed, the increase of radiative trapping due to the three-dimensional morphology of the cities, the energy storage in urban materials, the decrease of evapotranspiration in relation to the reduction of vegetalized and pervious surfaces in city centers and the heat release due to human activity [START_REF] Hidalgo | Advances in urban climate modeling[END_REF].

In order to investigate the relationships between urban fabric and local climate, scientists have developed different classifications such as the FRAISE ("Flux Ratio-Active Index Surface Exchange") scheme [START_REF] Loridan | Characterisation of energy flux partitioning in urban environments: links with surface seasonal properties[END_REF], which defines Urban Zones that characterize Energy partitioning (UZE). UZE classification involves the Surface Energy Balance (SEB) scheme [START_REF] Oke | Boundary Layer Climate[END_REF] and aims to distinguish urban areas in terms of how they partition incoming total downward radiation (which corresponds to solar radiation and longwave radiation). Since urban environment implies local climate modification, another relevant approach consists in incorporate urban indicators to design a climate classification. Urban Climatic Maps (UCMaps) are based on this approach. They are built using land use information, topographic information and climatic data (see [START_REF] Ren | Urban climatic map studies: a review[END_REF] for a review). Once produced, these maps provide enhanced climatic understanding of the region of interest and urban planning recommendations.

In the same manner, Urban Climate Zone (UCZ) is a simple urban classification scheme which uses urban descriptors dealing with urban morphology and land use [START_REF] Oke | Initial guidance to obtain representative meteorological observations at urban sites[END_REF]. UCZ scheme classifies urban areas regarding the way they modify the wind, thermal and moisture variables. This work has been extended by Stewart who further introduced Local Climate Zone (LCZ) [START_REF] Stewart | Redefining the urban heat island[END_REF]. A Local Climate Zones is defined as an area with a minimum diameter of 400 m which demonstrates both uniform features in terms of urban morphology, land use, urban material and urban metabolism, and a characteristic screen-height temperature regime under calm and clear sky. [START_REF] Stewart | Local climate zones for urban temperature studies[END_REF].

One of the major advantages of the LCZ scheme is the possibility to redefine urban heat island magnitude. As Stewart highlighted [START_REF] Stewart | Redefining the urban heat island[END_REF], the usual UHI definition can be discussed, on the one hand because the use of the adjectives "urban" and "rural" may be misleading, and on the other hand because these adjectives may be too simple to describe the complex layout of cities. Unlike the common definition, the LCZ scheme uses standardized descriptions of urban and rural landscapes and defines the UHI amplitude as the screen-height air temperature difference between two specific LCZ that are significantly different in terms of morphology and land cover. The use of UCZ and LCZ schemes has provided an overview of the air temperature distribution in Glasgow (UK) [START_REF] Emmanuel | Urban heat island and its impact on climate change resilience in a shrinking city: The case of glasgow, uk[END_REF], Toulouse (France) [START_REF] Houet | Mapping urban climate zone and quantifying climate behaviours -an application on toulouse urban area (france)[END_REF], Nagano (Japan), Vancouver (Canada) and Uppsala (Sweden) [START_REF] Stewart | Evaluation of the 'local climate zone' scheme using temperature observations and model simulations[END_REF]. Nocturnal difference of 2.6 have been observed in Toulouse between UCZ 2 and UCZ 7 whereas nocturnal difference from 2.8 to 5 have been recorded between LCZ 2 and LCZ D in Uppsala.

This work aims to apply the LCZ scheme in the Great Nancy Area (France) in order to investigate on what extent a homogeneous urban fabric leads to a specific thermal pattern under calm, clear sky. The air temperature distribution inside LCZ, the relationship between urban indicators and air temperature features, and the air temperature differences between LCZ types are particularly analyzed.

Materials and methods

Site description

Nancy conurbation, which numbers approximately 286,000 inhabitants, is located in the North-East part of France (Lat/Long: 48 41'N -6 11'E) in the center of the Lorraine plateau. North sea shore is about 400 km North-West, the Jura Mountains are about 200 km South. The general climate type is Cfb (i.e. temperate, with warm summer, without dry season) according to Köppen-Geiger climate classification [START_REF] Peel | Updated world map of the köppen-geiger climate classification[END_REF]. At mesoclimatic scale, Nancy is in a half-basin situation with two plateaus West and North-East and a plain South and East. The Meurthe river and the Marne-Rhine channel pass in the center of the urban area. July is the warmest month of the year, with an average air temperature between 13.7 (minimum) and 25.1 (maximum).

Micrometeorologic mobile survey

Mobile measurement method has been chosen since it allows to probe the spatial air temperature distribution inside LCZ. The mobile survey approach has been widely used in urban climate studies such in Lodz (Poland) [START_REF] Klysik | Temporal and spatial characteristics of the urban heat island of lodz, poland[END_REF], Szeged (Hungary) [START_REF] Unger | Temperature cross-section features in an urban area[END_REF], Utrecht (Netherlands) [START_REF] Brandsma | Measurement and statistical modeling of the urban heat island of the city of utrecht (the netherlands)[END_REF] or Leipzig (Germany) [START_REF] Schwarz | Relationship of land surface and air temperature and its implication for quantifying urban heat island indicators -an application for the city of leipzig (germany)[END_REF]. This field experiment targets to take advantage of this method, recording screen-height air temperature in numerous streets of a LCZ. Therefore car itineraries have been defined in order to investigate in details each selected LCZ and traverses have been realized both daytime and nighttime during summer 2012 [START_REF] Leconte | Methodology for semi-empirical climatic modelling using on-board measurements[END_REF] and summer 2013, each time between July and September.

In this context, the topography of the conurbation has been taken into account. Indeed, if a LCZ exhibits significant altitude changes, the itinerary needs to be adapted in order to estimate the influence of topography on the spatial air temperature distribution. A digital elevation map (resolution of 1 m) has been used to identify LCZ exhibiting significant altitude changes.

Measurements have been performed for low nebulosity (0 to 2 octas) and low wind speed (below 9 m.s -1 , wind speed at 10 m high), in anticyclonic conditions with dry road surface. In most of the case, these meteorological conditions were similar during the previous day. In case of rain, measurements have been postponed from 24 h at least.

One measurement session takes approximately 2 h 30 min. Considering the air temperature evolution that takes place at mesoclimatic scale during one traverse (about 2 at nighttime), a linear time-correction scheme has been applied. The air temperature at the beginning of the session is the reference temperature. The scheme consists in correcting the measured air temperature proportionally to the elapsed time between the beginning of the session and the measurement. This scheme offers the possibility to analyze mobile measurements in a synchronous manner. Starting hour for daytime measurements was 2pm (i.e. approximately solar noon), before the theoretical daily maximum air temperature hour, whereas the starting hour for nighttime measurements was 12am (i.e. approximately 3 h after sunset), before the theoretical maximum of UHI amplitude.

Mobile measurements have been completed with an instrumented vehicle. Air temperature at 2 m high have been recorded using a PT 100 probe with an accuracy of ±0.2 installed inside a ventilated cylinder mounted on the roof of the car. Measurements have been realized at a 3 m distance step.

During measurement sessions, the instrumented car is embedded in the traffic. Heat release from other vehicles may artificially increase the air temperature recorded and cause a bias in the data. In order to lower this effect, measurements points were excluded from the data set when the car was stopped. Therefore, measurements points in the data set correspond to a car speed between 15 km.h -1 and 60 km.h -1 .

LCZ building process

The first step of the LCZ building methodology, i.e. metadata gathering, provides a list of ten climatic indicators that are relevant to calculate or estimate, namely: Metadata can be collected through field visit, GIS calculations [START_REF] Houet | Mapping urban climate zone and quantifying climate behaviours -an application on toulouse urban area (france)[END_REF] or satellite data [START_REF] Bechtel | Classification of local climate zones based on multiple earth observation data[END_REF] [START_REF] Gamba | Urban climate zone detection and discrimination using object-based analysis of vhr scenes[END_REF]. The second step, i.e. source area definition, can be performed using either a footprint model [START_REF] Schmid | Footprint modeling for vegetative atmosphere exchange studies: a review and perspective[END_REF], or a "rule of thumb" approach which suggests that the size of the source area does not extent a few hundreds m [START_REF] Stewart | Local climate zones for urban temperature studies[END_REF]. In [START_REF] Stewart | Evaluation of the 'local climate zone' scheme using temperature observations and model simulations[END_REF], source area size is about 100 m radius for densely built area and 200 m radius for open land areas. For the third step, urban indicators calculations are used to choose the most relevant LCZ type amongst the 17 existing LCZ types (10 urbanized, 7 nonurbanized).

In the present study, the LCZ building process for the region of interest has consisted in two steps:

Firstly, an initial LCZ map has been established considering urban morphology and land use.

Secondly, LCZ contours have been adjusted by calculating seven urban indicators over ten. Sky view factor has been calculated using SAGA GIS (Böhner et al., 2006) and a Digital Surface Model (DSM) of the region of interest. Aspect ratio has been calculated using the expression proposed in [START_REF] Masson | A physically-based scheme for the urban energy budget in atmospheric models[END_REF]. Terrain roughness class has been determined for each LCZ according to the Davenport classification [START_REF] Davenport | Estimating the roughness of cities and sheltered country[END_REF]. Building surface fractions have obtained from urban GIS database. Impervious and pervious surface fractions have been manually calculated using visible satellite imagery data. Surface albedo, surface admittance and anthropogenic heat flux have not been estimated because of a lack of input data regarding urban materials and anthropic activities.

In addition, two Control Sites (CS) have been selected for each LCZ. The purpose of these CS is to study air temperature differences that can occur inside a LCZ and to provide information about how topoclimatic effects are controlled. A CS is defined as a portion of street, whose length is between 50 m and 100 m, which has been chosen in order to be representative of the overall LCZ surface in terms of urban indicators.

Control Sites have been built according to the following process: A Control Site can not be placed at the edge of the LCZ (minimum distance of 100 m between the Control Site and the border of the LCZ).

Urban elements which are not representative of the LCZ should be avoided (e.g. squares, fountains).

Control Site should be located in streets which are representative of the LCZ (e.g. backstreets).

Crossroads, stops and traffic light should be avoided.

The distance between two Control Sites of one single LCZ should be maximized.

Urban indicators calculations in the buffer zone of a Control Site adopt the same methodology as calculations at LCZ level, except that the terrain roughness class has not been calculated.

Post measurements process on air temperature data set

Several post-processing steps have been performed on the air temperature data set in order to produce thermal indicators. The following calculations consider N p as the number of measurements points recorded over a road section p and T air p,u the air temperature measured at the point u of the road section p. The air temperature amplitude of the road section p, written as ∆T air p , is defined as ∆T air p = max(T air p ) -min(T air p )

(1)

The spatial mean air temperature over the road section p, written as T air p , is defined as

T air p = 1 N p u=Np u=1 T air p,u (2)
The difference of spatial mean air temperature between LCZ X and LCZ Y for the road section p, written as ∆T air p,LCZ X-Y , is defined as ∆T air p,LCZ X-Y = T air p in LCZ X -T air p in LCZ Y

(3)

The difference to the spatial mean air temperature at the point u for the road section p, written as δT air p,u , is defined as δT air p,u = T air p,u -T air p (4)

Considering N LCZ as the number of traverses performed for a given LCZ, the average of the difference to the spatial mean air temperature, written as δT air u , is defined as

δT air u = 1 N LCZ p=N LCZ p=1 δT air p,u (5)
Considering N CSx as the number of measurements points recorded inside a given Control Site CSx over a road section p, the Control Site temperature, written as T air CSx,p , is defined as

T air CSx,p = 1 N CSx u=N CSx u=1 T air p,u (6)
The difference of temperature between two Control Sites (CS1 and CS2) for the road section p, written as ∆T air CS,p , is defined as ∆T air CS,p = T air CS1,p -T air CS2,p

The average temperature of two Control Sites for the road section p, written as T air CS,p , is defined as T air CS,p = T air CS1,p + T air CS2,p 2 (8)

Results

Indicators calculations and LCZ map of Nancy

LCZ map of the Great Nancy Area (GNA) is presented Figure 1. Due to the specific urban features of the Great Nancy Area, only 10 types of LCZ (over the 17 existing types of LCZ) have been used. Thirteen LCZ of different types have been selected for the field experiment. The chosen locations vary from downtown Nancy to the periphery of the region of interest. Urban indicators values are presented from Figure 2 to Figure 6.

Several residential areas, namely JAR, SEI and PUL, are on the edge between type 6 and type 9 in terms of land use fractions (Figure 4). Morphological indicators values do not allow to determinate a LCZ type, since sky view factor values are type 6 and aspect ratio values are type 9. In this ambiguous situation, any choice has been performed. Consequently these three LCZ are considered as type 6/9. In the case of MBR and PVE (Figure 5), land use indicators do not exactly match with type 8 values. However, the text definition and illustrations of the LCZ type 8 clearly argue for identifying these two LCZ as a type 8. Thus MBR and PVE have been considered as LCZ type 8. In non-urbanized LCZ (CE1 and CE3, Figure 6), sky view factor has not been calculated because the required satellite data was not available. Nevertheless, field visits indicate that the sky view factor values for these LCZ are very likely to be into the accurate range of values.

Except the three equivocal LCZ type 6/9, urban indicators calculated both at LCZ level and at Control Site level generally fall into the suggested range of values for the corresponding type of LCZ. Indicators that are out of the suggested range of values are reasonably close. The maximum distance between calculated values and suggested values is 0.07 for sky view factor, 0.07 for aspect ratio, and 7% land use fractions. This result confirms that the selected LCZ building process is suitable for this study, since existing urban areas have been identified to LCZ type with acceptable mismatch.

Besides, urban indicators values calculated inside of the Control Sites buffer zone are generally close to urban indicators values for the global LCZ. A few gaps between CS values and LCZ values are observed for the sky view factor indicator. This comes from slight differences in sky view factor calculation method between Control Site (street canyon sky view factor) and LCZ (area-averaged sky view factor). Overall, it seems that Control Sites locations have been chosen appropriately in terms of urban indicators representativeness.

At the end of the LCZ building process, the sum of all LCZ surface does not cover all the region of interest. The reason is that several areas do not meet the LCZ scheme requirements, either because their diameter is smaller than 400 m, or because they present important inhomogeneities in surface structure, cover, fabric and metabolism in a way that they can not be identified to a specific LCZ type (hatched areas in Figure 1(b)). 3.2. Mobile measurements outcome: spatial variability of air temperature inside LCZ 3.2.1. Air temperature amplitude inside LCZ

The air temperature amplitude ∆T air p in the 13 selected LCZ is presented in Figure 7. During daytime, air temperature amplitude in LCZ type 2 and 5 tend to be higher than in LCZ type 8, 6/9 and D (Figure 7(a)). In LCZ type 2 and 5, amplitude values are scattered over 2 while in LCZ type 8, 6/9 and D amplitude values are more often comprised within 1.

Unlike the other LCZ types used in the Great Nancy Area, LCZ type 2 and 5 exhibit specific values of morphological indicators. Sky view factor is always below 0.7, aspect ratio is above 0.35, mean building height is above 10 m and terrain roughness class is 7. These particular morphological features may explain the observed air temperature amplitude distribution. Due to urban morphology and street orientation, daytime total direct solar radiation that reaches urban materials varies from one street to another. This difference in terms of incoming radiations between streets can lead to a more heterogeneous air temperature distribution within LCZ type 2 and 5.

In addition, anthropogenic flux may be more important in LCZ type 2 and 5 during daytime, which may lead to a positive air temperature anomaly (called hotspot). Conversely, vegetated areas located in dense and urbanized LCZ types could imply a negative air temperature anomaly (called coldspot) due to significant evapotranspiration in daytime. Both phenomenons may lead to a greater daytime air temperature amplitude inside LCZ.

Except for SEI and LCZ type D, air temperature amplitude is mainly between 0.5 and 2 during nighttime (Figure 7(b)). Air temperature amplitude in LCZ type 2 and 5 as well as in JAR tend to be lower in nighttime than in daytime, whereas air temperature amplitude in LCZ type 8, PUL and CE3 seems to be similar between the two periods. The general trend shows that in all urbanized types of LCZ, air temperature distribution tends to be more homogeneous in nighttime, i.e. with a lower air temperature amplitude. Greater nocturnal air temperature amplitude in SEI is very likely due to cold air advection from a contiguous LCZ type D. Greater nocturnal air temperature amplitude in CE1 is probably driven by a topoclimatic effect. Indeed, a low point (i.e. an altitude drop of 14 m over a portion of road of 600 m) tends to lead to an air temperature drop.

OUE

Recurrent hotspots and coldspots inside LCZ

Figure 8 aims to locate hotspots and coldspots within JAR and NAT, presenting the average over 11 traverses of the difference to the spatial mean air temperature δT air u . JAR does not reveal recurrent climate inhomogeneities, neither daytime nor nighttime, since the dispersion of δT air u is tight (between -0.2 and 0.2). Conversely, NAT offers a greater dispersion of δT air u (between -0.6 and 0.4 in daytime, between -0.9 and 0.5 in nighttime). In this latter LCZ, a few areas are recurrent hotspots and coldspots relatively to the spatial mean air temperature. These climatic inhomogeneities should be related to peculiarities in terms of urban features. For instance in Figure 8(d), the nocturnal coldspot (dark blue area south of the LCZ) is a tree-planted avenue while the nocturnal hotspot (bright red area in the middle of the LCZ) is localized in an impervious environment. In general, small LCZ with homogeneous urban morphology and indicators (e.g. JAR) have less hotspots and coldspots than large LCZ with heterogeneous urban morphology (e.g. NAT).

Another noticeable element is that nocturnal hotspots and coldspots do not systematically match with diurnal hotspots and coldspots. This result highlights that the thermal difference to the spatial mean air temperature evolves considerably throughout the time at microscale, under the influence of numerous parameters such as street orientation, local winds and anthropogenic heat release.

Thermal behavior of Control Sites

Results show that air temperature differences between two Control Sites of one given LCZ are acceptable. 96% of the ∆T air CS,p values are between -1 and 1, and 74% are between -0.5 and 0.5. This fact illustrates that in general topoclimatic effect seems to have been controlled and that Control Site have been accurately chosen.

The difference between the spatial mean air temperature T air p and the arithmetical average of the two Control Sites T air CS,p is presented Figure 9. Except two traverses in PVE and NAT, the difference between these two indicators is equal or below 0.5 both daytime and nighttime. T air p and T air CS,p are very close, i.e. the arithmetical average of the Control Sites represents fairly well the spatially averaged air temperature of an LCZ.

Synchronous mean air temperature comparison between LCZ

Table 1 presents the summary of the synchronous mean air temperature ∆T air LCZ X-Y , both daytime and nighttime. At daytime, all ∆T air LCZ X-Y are below 1. LCZ types 2 and 8 are the hottest LCZ type while LCZ type D seems to be the coldest LCZ type. These experimental results correspond to the literature, which states that the urban heat island amplitude is limited at mid-afternoon. At nighttime, LCZ types may be separated into three groups, namely LCZ types 2 and 5 (group 1), LCZ types 8 and 6/9 (group 2) and LCD type D (group 3). Average air temperature difference between group 1 and group 2 is from 1.3 to 1.8 while average air temperature difference between group 1 and group 3 is from 4.2 to 4.4. Similarly to daytime, LCZ type 2 is the hottest LCZ type while LCZ type D is the coldest LCZ type.

The characteristics of the LCZ types provide explanations about these nocturnal temperature difference between LCZ. Indeed, considering the indicators values for the studied LCZ, common features can be found within each group. As introduced in section 3.2.1, similar pattern for morphological indicators can be found for group 1. It is also the case for LCZ group 2, which shows different values from group 1: sky view factor is always above 0.6, aspect ratio is below 0.2, mean building height is below 10 m and terrain roughness class is 6 or below. Regarding land use, values from group 1 are scattered whereas it is possible to notice that building surface fraction is always below 30% within group 2. Another important point is that the studied LCZ of group 1 are located in the geographical center of the urban area while LCZ of group 2 are mostly located in the periphery of the city. Group 3, i.e. LCZ type D, is significantly different than the other LCZ types studied, with low height vegetation and without any buildings.

Discussion

The LCZ building process performed in the Great Nancy Area have risen several challenges. Since the region of interest is a continuously urbanized territory, the determination of the LCZ contours have not been always straightforward. While some LCZ are very homogeneous in terms of urban indicators (JAR), some others are more heterogeneous (NAT), with important local variations of urban indicators. Local knowledge of the urban fabric have been required at this step. Besides, LCZ map of Nancy shows that many LCZ have a diameter slightly above 400 m. LCZ with large diameter, LCZ Type 2 5 6/9 8 D 2 -0.2 (0.5) 1.8 (0.6) 1.5 (0.6) 4.4 (1.0) 5 0.4 (0.3) -1.5 (0.6) 1.3 (0.7) 4.2 (1.0) 6/9 0.3 (0.3) -0.1 (0.4) --0.3 (0.4) 2.4 (0.7) 8 0.0 (0.3) -0.3 (0.3) -0.2 (0.3) -2.9 (0.7) D 0.8 (0.5) 0.5 (0.4) 0.6 (0.4) 0.8 (0.4) - i.e. above 1000 m, are rare. This constellation of small LCZ is due to the medium size of the conurbation as well as the history of urban development of the city.

Quality and availability of input data are crucial for the indicators calculation process. Up-to-date satellite maps as well as appropriate GIS algorithms have been key elements. This study has demonstrated the possibility to build LCZ using 7 indicators over 10. Unfortunately, the calculation of the surface albedo, surface admittance and anthropogenic heat flux have not been realized. However, these three urban indicators are not distinct for the urbanized LCZ types used in this study. For instance, range of values for the surface albedo is identical for the LCZ types 5, 6 and 9. Therefore it is impossible to know if their calculation would have lead to a more accurate LCZ identification process in the present case.

This work also highlights that the average air temperature difference between pairs of LCZ types can exhibit values of the same order of magnitude than the air temperature amplitude inside LCZ. Indeed, the air temperature amplitude ∆T air p inside LCZ is sometimes about 2 at nighttime, and in the meantime, nocturnal average ∆T air LCZ 2-6/9 is 1.8. This result demonstrates that air temperature anomalies at microscale may be significant in comparison to the local scale thermal pattern. As presented section 3.2.2, the study of recurrent hotspots and coldspots inside LCZ can be particularly relevant to locate these inhomogeneities.

The accuracy of the sensor and the measured diurnal values of ∆T air LCZ X-Y between close LCZ types ( [START_REF] Stewart | Redefining the urban heat island[END_REF].

allow to use a more accurate technology. Thus, daytime measurements performed in this study can only demonstrate that ∆T air LCZ X-Y between close LCZ types are below 1.

Measured nocturnal values of ∆T air LCZ X-Y in the Great Nancy Area are compared with Stewart's observations in Table 2. In both cases, a small (resp. large) difference in morphology and fabric corresponds to a small (resp. large) air temperature difference. The range of values obtained in the present study are slightly lower than Stewart proposed. This could be explained by the size of Nancy, which is a relatively small comparing to cities like Vancouver or Nagano. However, nocturnal ∆T air LCZ X-Y values in Nancy are similar to the values recorded in Uppsala [START_REF] Stewart | Evaluation of the 'local climate zone' scheme using temperature observations and model simulations[END_REF], which is also an European middle-size city.

Conclusion

This paper aims to contribute to deepen knowledge about air temperature features within LCZ and between pairs of LCZ. To that purpose: LCZ scheme has been applied in the Great Nancy Area and a LCZ map has been built. Indicators calculations have allowed to identify existing urban areas to specific types of LCZ. Spatial air temperature distribution have been investigated inside LCZ. At nighttime, air temperature amplitude is generally between 0.5 and 2 in urbanized LCZ types. Within the LCZ, Control Sites average temperature T air CS,p has demonstrated good similarities with the spatially averaged air temperature.

Observed microscale thermal inhomogeneities illustrate the existing relationships between urban district layout and screen-height air temperature behavior. Recurrent hotspots and coldspots have been located in LCZ which present heterogeneous urban fabric. This relationship is also noticeable at mesoclimatic scale through mean air temperature comparisons between LCZ types. Values of ∆T air LCZ X-Y range between less than 1 for close LCZ types to 4.4 for dissimilar LCZ types.

Concerning the experimental approach, mobile surveys have recorded LCZ climate features and have provided high density data. Results indicate that the determination of hotspots and coldspots and the study of climate inhomogeneities in LCZ requires high spatial density data that mobile survey can provide.

Initially, LCZ scheme was built notably to guide UHI studies and improve results communication amongst urban climate researchers. However, this scheme can also be adopted in order to set up a meteorological network over a conurbation, placing meteorological stations within LCZ of interest. In this context, the mobile measurements approach can be relevant since it allows to propose stations sites (e.g. control sites) and to locate non-representative sites (hotspot and coldspot). Furthermore, this simple and intuitive scheme, which has been designed for urban climate observations, could provide a valuable asset to urban planning strategies. Indeed, a comprehensive LCZ map could locate areas that are potentially warmer than others during calm and clear nights in summertime. These specific urban areas could be elected to implement countermeasures to urban heat island.

Aknowledgments

This work has been supported by the French Environment and Energy Management Agency (ADEME) and the GEMCEA.

  Figure 1: (a) Map of the Great Nancy Area. Scale 1:55000. (b) LCZ map of the Great Nancy Area. The 13 selected LCZ for the field experiment and the routes performed in 2012 and 2013 are displayed. The number of traverses per LCZ are in parenthesis (number of diurnal traverses/number of nocturnal traverses). Scale 1:55000.

-

  Surface admittance (J.m -2 .s 0.5 .K -1 ) -Surface albedo (-) Anthropogenic indicator -Anthropogenic heat flux (W.m -2 )

  Within a buffer zone of 100 m around a Control Site, urban indicators values should match with the average urban indicators values of the LCZ.

Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 23456 Figure 2: (a) Indicators range for a LCZ type 2 Compact Midrise. Indicators values for OUE, CEN, TRM. (b) Street view in OUE (e) Airborne view of OUE. Scale 1:5000.

Figure 7 :

 7 Figure 7: Air temperature amplitude ∆T air p inside the 13 selected LCZ, sorted by LCZ type. Red triangles correspond to traverses performed in 2012. Blue circles correspond to traverses performed in 2013. (a) Diurnal period. (b) Nocturnal period.

Figure 8 :Figure 9 :

 89 Figure 8: Average over 11 traverses of the difference to the spatial mean air temperature δT air u . (a) Diurnal period in JAR. (b) Nocturnal period in JAR. (c) Diurnal period in NAT. (d) Nocturnal period in NAT.

Table 1 :

 1 Average difference between pairs of LCZ types ∆T air LCZ X-Y . Bottom left area is the difference between the LCZ type in column minus the LCZ type in row and corresponds to the daytime period. Upper right area is the difference between the LCZ type in row minus the LCZ type in column and corresponds to the nighttime period. Standard deviation are in parenthesis.

Table 1

 1

	) exhibit the same order of mag-

Table 2 :

 2 Nocturnal average difference between pairs of LCZ types measured in the Great Nancy Area compared to Stewart's observations


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Computational study of shock waves reflection over concave double wedges reflectors

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Computational and experimental studies of shock waves reflection over concave double wedges reflectors

Vineet Soni [START_REF] Ben-Dor | Shock Wave Reflection Phenomena[END_REF] Meital Geva 2 Omri Ram 2 Abdellah Hadjadj [START_REF] Ben-Dor | Shock Wave Reflection Phenomena[END_REF] Oren Sadot 2 Gabi Ben-Dor The study aims at understanding the phenomenon of shock reflection over different geometries of concave surfaces. The double concave wedge is generated computationally by changing different geometrical parameters like radius and initial wedge angle of the two concave surfaces. The study focuses on the effects of the change of the geometry of the concave surfaces and the incident shock wave Mach numbers on the overall behavior of shock wave reflections. The simulations are done for four different incident shock wave Mach numbers. The investigation of the effect of different geometrical and flow parameters reveals a number of interesting wave features starting from the early stage of the shock interaction and transition from Mach reflection (MR) to transitioned regular reflection (TRR) over the first concave surface followed by complex shock reflections over the second concave surface. Two new behaviors of the shock reflections are found over the second concave surface, which appear to depend on the initial wedge angle of the concave surfaces. In the first behavior, an additional shock wave appeared when the TRR over the first concave surface meets the incident shock wave on the second concave surface and eventually merges with the reflected wave as shown in figure 1(a). In the second behavior, the additional shock wave falls behind the incident shock wave and penetrates the reflected wave over the second concave surface as shown in figure 1(b). Also, it was found that the difference between the transition wedge angles over the two concave surfaces increased as the incident shock wave Mach number increased. These findings indicate that the shock wave does have a characteristic to retain its memory.

Figure 1 :

 1 Figure 1: Shock waves reflection: the radius of the concave surfaces is 50mm; the incident shock wave Mach number is 1.6


Lionel Fontan
email: lfontan@archean.tech

Shinyoung Kim

Verdiana De Fino
email: verdiana.defino@irit.fr

Sylvain Detey
email: detey@waseda.jp

Predicting speech fluency in children using automatic acoustic features

The present study aims at predicting the speech fluency of children using automatic acoustic measures derived from forward-backward divergence segmentation (FBDS). Thirteen Korean children were recorded while reading out loud a set of sentences. Three native-Korean speakers evaluated the fluency of each sentence on a five-point scale. A FBDS algorithm was used to segment speech recordings into sub-phonemic units and silent segments. In addition to the low-level acoustic features directly derived from FBDS segments, higher-level acoustic features were computed by clustering FBDS segments into pseudo-syllables and silent breaks. Both low-and higher-level features were used to predict average ratings of speech fluency, using a leave-one-speaker-out cross-validation scheme and three regression models: a multiple linear regression, a support vector regression, and a random-forest regressor. Highly accurate predictions were achieved, with average root-mean-square errors (RMSEs) as low as 0.3. Prediction accuracy did not significantly change as a function of regression model. Using higher-level features yielded lower RMSEs than using raw FBDS features. The results of a multiple linear regression using higher-level features (R 2 = 0.94) suggest that speech/silence ratio and pseudo-syllable rate are the two most important predictors of speech fluency.

I. INTRODUCTION

Measures of speech fluency are useful tools for monitoring speech production skills during first (L1) and second language (L2) acquisition, as they indicate the extent to which speech "flows easily without pauses and other disfluency markers" [START_REF] Derwing | Pronunciation Fundamentals. Evidence-based Perspective for L2 Teaching and Research[END_REF]. However, subjective evaluations of speech fluency are timeconsuming since raters (e.g., teachers) have no choice but to assess speech productions individually, that is, one speaker at a time. To cope with this issue, early studies proposed using automatic speech recognition (ASR) to compute rapid and objective estimates of speech fluency. For example, in the context of L2 acquisition, ASR systems were used to compute speech rate and speech/silence ratio estimates that were strongly correlated with subjective ratings of speech fluency [START_REF] Cucchiarini | Quantitative assessment of second language learners' fluency by means of automatic speech recognition technology[END_REF][START_REF]Quantitative assessment of second language learners' fluency: Comparisons between read and spontaneous speech[END_REF].

However, an important drawback of ASR systems is that they can be challenged by noncanonical speech. ASR systems are usually trained using speech samples produced by healthy, native adult speakers, and their recognition accuracy decrease when processing nonnative (L2) speech [START_REF] Vu | Improving ASR performance on non-native speech using multilingual and crosslingual information[END_REF], pathological speech [START_REF] De Russis | On the impact of dysarthric speech on contemporary ASR cloud platforms[END_REF], or speech produced by children [START_REF] Gelin | End-to-end acoustic modeling for phone recognition of young readers[END_REF]. Using ASRbased features to predict speech fluency in such populations can thus be hazardous, as word-recognition errors may bias the measurement of speech-fluency predictors (e.g., speech rate can be under-or overestimated depending on the length of the words recognized by the system). To overcome this shortcoming, some authors used low-level, temporal acoustic analyses to directly estimate predictors of speech fluency such as speech/silence ratio, speech rate, rate of silent breaks and presence of hesitations [START_REF] Lustyk | Evaluation of disfluent speech by means of automatic acoustic measurements[END_REF][START_REF] Fontan | Automatically measuring L2 speech fluency without the need of ASR: A proof-of-concept study with Japanese learners of French[END_REF][START_REF] Detey | Computer-assisted assessment of phonetic fluency in a second language: a longitudinal study of Japanese learners of French[END_REF]. In particular, forward-backward divergence segmentation (FBDS [START_REF] André-Obrecht | A new statistical approach for the automatic segmentation of continuous speech signals[END_REF]) was successfully used for predicting speech fluency in adult L2 speakers [START_REF] Fontan | Automatically measuring L2 speech fluency without the need of ASR: A proof-of-concept study with Japanese learners of French[END_REF][START_REF] Detey | Computer-assisted assessment of phonetic fluency in a second language: a longitudinal study of Japanese learners of French[END_REF] as well as in adults with speech disorders [START_REF] Sztahó | Speech Fluency Measurement of Patients with Parkinson's Disease by Forward-Backward Divergence Segmentation[END_REF]. The FBDS algorithm detects significant changes in the trajectory of the signal energy over time (e.g., abrupt increases or decreases in signal energy) and, when applied to speech, results in a subphonemic segmentation of the input signal. To measure speech fluency, authors either used low-level predictors directly based on FBDS segments (e.g., number of FBDS speech segments per second as a measure of speech rate [START_REF] Fontan | Automatically measuring L2 speech fluency without the need of ASR: A proof-of-concept study with Japanese learners of French[END_REF][START_REF] Detey | Computer-assisted assessment of phonetic fluency in a second language: a longitudinal study of Japanese learners of French[END_REF]) or predictors that required a clustering of FBDS segments into higher-level units such as silent breaks and pseudo-syllables [START_REF] Farinas | Automatic rhythm modeling for language identification[END_REF] (e.g., pseudosyllable rate [START_REF] Fontan | Building an ASR-free automatic tool for measuring the speech fluency of Japanese learners of English[END_REF][START_REF] Kondo | Phonetic fluency of Japanese learners of English: automatic vs native and non-native assessment[END_REF][START_REF] Fontan | Using the forwardbackward divergence segmentation algorithm and a neural network to predict L2 speech fluency[END_REF]).

However, as none of the previous studies used both lowlevel and higher-level predictors, it is not clear if the clustering of FBDS segments into higher-level units provide any significant benefit for the prediction of speech fluency. Also, another limit of previous studies is that only linear combinations of features (e.g., multiple linear regressions) were used to predict speech fluency. Yet, it is possible that the importance of a given predictor depends on other predictors (e.g., one could assume that the presence of silent breaks has a lesser impact on speech fluency when speech rate is already very low) -in which case using nonlinear models could improve the prediction accuracy. Finally, so far, the FBDS algorithm was only used to predict speech fluency in adult speakers, despite the numerous clinical and educational applications that could be developed for children using such techniques (e.g., for the automatic assessment of children literacy during read-aloud tasks).

Therefore, the main aim of the present study is to establish the proof-of-concept that the FBDS algorithm can be used to accurately predict speech fluency in children. A second objective of the study is to overcome the methodological limits of previous research works by 1) determining whether the clustering of FBDS segments into pseudo-syllables and silent breaks significantly improves the prediction of speech fluency by comparison with the use of low-level FBDS features alone, and 2) if nonlinear regression models yield more accurate predictions of speech fluency than a multiple linear regression. A final contribution of this work is to analyze regression results to determine which features contribute the most to the prediction of speech fluency.

II. SUBJECTIVE RATINGS OF SPEECH FLUENCY

A. Speech material

In order to collect reference data bearing enough variability in terms of speech fluency, 13 Korean children (6 female) of different ages and different levels of language exposure were recruited. Their age ranged from 9 to 12 years (mean: 10 years and 11 months; standard deviation, SD: one year). All were born and raised in Tokyo (Japan) under Korean parents and were attending local Japanese schools at the time of their participation. They all used Korean and Japanese, with different amounts of each language used at home depending on each family.

During the recording task, the children were seated in a quiet room in their homes, at their desks. They were instructed to read out loud an excerpt from The Giving Tree [START_REF] Silverstein | The giving tree[END_REF], a children's book translated in Korean, which consisted of 9 sentences.

The children were recorded using an ECM-MS957 electret condenser microphone (SONY, Tokyo, Japan). The microphone was placed in front of each child, at a distance of approximately 20 cm. Prior to the study, which complied with the ethical guidelines of Waseda University (Tokyo, Japan), all children's parents provided their informed consent.

B. Rating procedure

The first five sentences pronounced by the children (mean length of the sentences: 7.6 words; SD: 3.6) were used for the fluency rating task, for a total of 65 sentences (5 sentences × 13 children). Three female raters, all of them native speakers of Korean, participated in the assessment. Their age was comprised between 25 and 28 years. None of them reported any history of hearing difficulties.

Speech recordings were presented to each rater in a random order, using the Prodigy software (ExplosionAI GmbH, Berlin, Germany) version 1.11.7. Each rater assessed twice the whole 65 speech recordings, using a five-point scale in which 1 and 5 corresponded to the lowest and highest degree of speech fluency, respectively.

Prior to the rating procedure, the raters were familiarized with the concept of speech fluency. In particular, they were made aware that their ratings of speech fluency should not be influenced by the ability of the speakers to pronounce Korean phonemes -a poor fluency being possibly associated with a correct segmental production, and vice-versa. The raters also listened to several examples of utterances illustrating the whole fluency range. These examples were not part of the material later used in the rating task.

III. PREDICTION OF SPEECH FLUENCY

To predict ratings of speech fluency, the audio recordings were first segmented using FBDS. Then, FBDS segments were clustered into silent breaks and pseudo-syllables, and low-level and higher-level predictors were computed. Finally, three regression models were used to predict speech fluency ratings. The whole procedure is detailed in the following subsections.

A. Automatic segmentation of speech signals

Three steps were used to segment the speech signals into FBDS segments, pseudo-syllables and silent breaks (Fig. 1). During the first step, FBDS was applied. The principle of FBDS is as follows. The FBDS algorithm uses two windows: a short-term analysis window, and a longer-term window used as a buffer to store the current FBDS segment. The signals contained in the two windows are each modeled by an autoregressive Gaussian model. As the short-term window is used to progressively scan the input signal, the distance between the two Gaussian models (measured by the Kullback-Leibler divergence measure [START_REF] Kullback | On information and sufficiency[END_REF]) is used to detect segment boundaries. Each time a boundary is detected, the long-term window buffer is flushed, and the analysis goes forward. This process is eventually carried out backwards, and the boundaries found during forward and backward analyses are merged together. In a second step, the segments found by the FBDS algorithm were classified as either speech segments or silent segments, depending on their maximum energy exceeding 4% of the maximum energy found in the whole recording (in which case the segment was classified as speech), or not (in which case the segment was classified as silence).

In the third step, FBDS speech segments and FBDS silent segments were clustered into pseudo-syllables and silent breaks, respectively. Two consecutive FBDS speech segments were considered as part of the same pseudo-syllable if the average signal energy did not decrease more than a given ratio when switching from the first segment to the next. To define silent breaks, consecutive silent FBDS segments were merged together. All resulting silence clusters that lasted more than 250ms were considered as silent breaks, as this threshold was shown to be optimal for the measurement of speech fluency [START_REF] Anderson | Essentials of Linguistics[END_REF]. Fig. 2 shows, as an example, the results of the automatic segmentation of a speech signal corresponding to the three first words of the target text pronounced by one of the children.

B. Computation of predictors of speech fluency

Both low-level and higher-level predictors of speech fluency were computed. Low-level predictors were computed using FBDS speech segments and FBDS silent segments, that is, using the information directly available after the step 2 shown in Fig. 1. These low-level predictors included:

• Rate of FBDS speech segments: the number of FBDS speech segments divided by the duration of the recording. This feature provides an estimate of speech rate, and is thus expected to be positively correlated with speech fluency; • Standard deviation of FBDS speech segments duration.

As the standard deviation of speech segments increases with the presence of hesitations (filled pauses, i.e., sustained vowels), this feature is expected to be negatively correlated with speech fluency; • Rate of FBDS silent segments: the number of FBDS silent segments divided by the duration of the recording. It is assumed that the higher the rate of FBDS silents, the lower the speech fluency; • Speech ratio: the total duration of FBDS speech segments, divided by the duration of the recording. A high speech ratio is supposed to be indicative of a high speech fluency, and vice-versa. Higher-level predictors were computed based on the information available after the clustering of FBDS segments into pseudo-syllables and silent breaks (step 3 in Fig. 1). Higher-level predictors included:

• Rate of pseudo-syllables: the number of pseudo-syllables divided by the duration of the recording; • Standard deviation of pseudo-syllable duration;

• Rate of silent breaks: the number of silent breaks divided by the duration of the recording. Consistent with the assumptions made for low-level features, the rate of pseudo-syllables is expected to be positively correlated with speech fluency, whereas standard deviation of pseudo-syllables and rate of silent breaks are assumed to be negatively correlated with speech fluency.

C. Application of regression models

Three regression models were finally used to predict speech fluency ratings: a multiple linear regression (MLR), and two nonlinear models -a support vector regression (SVR) using a Radial Basis Function, and a random forest regressor (RFR). All models were implemented and evaluated using the Scikit Learn Python library [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF], version 0.24.2.

Two sets of predictors were used with each model (Table 1). The low-level predictor set contained the three predictors that were calculated based on FBDS segments. The higher-level predictor set contained the three predictors that were calculated based on pseudo-syllables and silent breaks. Both sets contained speech ratio, as this measure does not change when calculated using low-level or higher-level units (i.e., using FBDS speech segments or pseudo-syllables).

As the number of speech samples did not allow for the creation of a separate validation set, hyperparameters of the SVR (regularization parameter, gamma, and epsilon) and RFR Fig. 2. Spectrogram corresponding to the first three words of the target text ("옛날에 한 그루의"-"Once upon a time there was a (tree)") pronounced by one of the children, along with the corresponding phonetic transcription based on the International Phonetic Alphabet, and the FBDS segment boundaries, pseudo-syllables and silent breaks that were automatically identified. (number of trees and maximum depth of the trees) models were tuned using a nested cross-validation procedure.

Both the outer and inner cross-validation loops followed a leave-one-speaker-out (LOSO) scheme. The results of the inner loop were used to select optimal parameters. These parameters were fixed during the final LOSO cross-validation that was carried out to compare the performance of the three models.

IV. RESULTS

A. Reliability of subjective ratings of speech fluency

To assess intra-rater reliability, Spearman's rank correlation coefficients (rhos) and Cronbach's alphas (α) were computed on the two series of ratings provided by each rater for the 65 sentences (Table I). For the three raters, very strong correlations (all rhos ≥ 0.89), and very high alphas (all ≥ 0.95) are observed, indicating an excellent reliability.

To assess inter-rater reliability and agreement, the two series of ratings provided by each rater were first averaged. The results indicate a very high reliability, with a Cronbach's α equal to 0.97, and Spearman correlation coefficients ≥ 0.86 (Table II).

Inter-rater agreement was finally assessed by analyzing the distribution of fluency ratings (Table III). The aim of this analysis was to determine if all three raters used "the same yardstick" [START_REF] Cucchiarini | Quantitative assessment of second language learners' fluency by means of automatic speech recognition technology[END_REF] when evaluating speech fluency -case in which a straightforward combination of ratings (i.e., the computation of mean fluency ratings across raters) could be carried out to produce the final reference ratings. The descriptive statistics provided in Table III show that the distribution of fluency ratings is very similar across raters, with a maximum difference between mean ratings equal to 0.18 (for raters 1 and 2). As a Kruskal-Wallis test confirmed that there was no significant difference between the distributions of the ratings provided by the three raters (H(2) = 1.04; p = 0.6), the ratings were eventually averaged across raters. The final 65 speech-fluency ratings ranged from 1 to 5, with a mean value of 3.8 (SD = 1.4).

B. Bivariate correlations between individual predictors and speech fluency

The relationship between each predictor and average fluency ratings was assessed through nonparametric (Spearman) correlations (Table IV), as a Kolmogorov-Smirnov test indicated that the ratings were not normally distributed (p < 0.01). As was assumed, measures of speech rate and speech ratio are positively correlated with ratings of speech fluency: the higher the fluency, the faster the speech rate and the higher the speech ratio. Standard deviations of speech segments (FBDS speech segments and pseudo-syllables) are, on the contrary, negatively correlated with ratings of speech fluency. The same is true for the two measures of the rate of silences (FBDS silent segments and silent breaks): the higher the rate of silent segments, the lower the speech fluency. Altogether, the strongest correlations are observed for speech ratio, rate of silent breaks and rate of pseudo-syllables (all absolute rhos ≥ 0.88).

C. Prediction of speech fluency

Table V shows the average root-mean-square errors (RMSEs) found for each predictor set and regression model. In general, predictions of speech fluency are accurate, with average RMSEs close to, or even inferior to one tenth of the speech-fluency scale (i.e., to 0.4). This accuracy is higher than in previous studies that used comparable methods to predict the speech fluency of adult L2 learners, in which the lowest RMSE was 0.51 [START_REF] Fontan | Building an ASR-free automatic tool for measuring the speech fluency of Japanese learners of English[END_REF][START_REF] Kondo | Phonetic fluency of Japanese learners of English: automatic vs native and non-native assessment[END_REF][START_REF] Fontan | Using the forwardbackward divergence segmentation algorithm and a neural network to predict L2 speech fluency[END_REF]. In the present study, the smallest RMSEs are found for the SVR model and the higher-level predictor set.

D. Effects of predictor set and regression type on prediction accuracy

As can be observed from Table V, for MLR and SVR models, the average RMSE tends to be lower when using higher-level predictors. The RFR model follows a different trend with a slight increase of the average RMSE when switching from low-level to higher-level predictors. Depending on the predictor set, the ranking of the regression models in terms of accuracy changes (RFR > SVR > MLR and SVR > MLR > RFR for the low-level and higher-level predictor sets, respectively).

To assess the statistical significance of the RMSE differences observed as a function of the predictor set and regression a linear mixed model was computed using SPSS Statistics version 23.0 (IBM, Armonk, NY). Predictor set and Regression model factors were treated as fixed effects, while Speaker factor (i.e., the speaker for whom the prediction was made in the LOSO setup) was considered as a random effect. The results indicate that Predictor set has a significant effect on the RMSE (F(1, 72) = 5.3; p = 0.02), contrary to Regression model (F(2, 72) = 1.2; p = 0.3). The interaction between Predictor set and Regression model just falls below the 5% level of significance (F(2, 72) = 3.1; p = 0.049).

E. Individual contribution of higher-level predictors

To evaluate the role of each higher-level feature for the prediction of speech fluency, a MLR was computed on the whole dataset (Fig. 2). The very high coefficient of determination indicates that the model explained 94% of the variability in fluency ratings. The standardized coefficients (std. coef.) of the regression equation show that speech ratio and rate of pseudo-syllable are the two most important predictors of speech fluency (std. coefs. of 0.52 and 0.49, respectively). Rate of silent breaks had a smaller predictive power (std. coef. = -0.31), and standard deviation of pseudosyllable duration only played a marginal role for the prediction of speech fluency (std. coef. = -0.08).

V. CONCLUSIONS

The present study demonstrated that FBDS-based automatic acoustic measures can be used to achieve highly accurate predictions of read-speech fluency in children. The study also demonstrated that clustering FBDS segments into higher-level units -namely, pseudo-syllables and silent breakssignificantly improves the prediction of speech fluency by comparison with the use of FBDS segments alone. This is all the more interesting since the method used for clustering FBDS segments into pseudo-syllables is theoretically relevant for all the languages in which syllables consist of vocalic nuclei, that is, for the vast majority of existing languages [START_REF] Anderson | Essentials of Linguistics[END_REF].

Another assumption was that using a nonlinear regression model might yield more accurate predictions than an MLR. The results failed to validate this hypothesis. This is not very surprising when considering that the MLR model could already account for 94% of the variance in speech-fluency ratings and achieved very low RMSEs (0.46 and 0.30 using low-and higher-level predictors, respectively), which left very little room for improvement. The analysis of the MLR coefficients showed that the two most important predictors of speech fluency were speech ratio and speech rate.

Altogether, the results of this study open the way to the use of FBDS-derived measures of speech fluency for numerous applications dedicated to children, whether educational (e.g., to assess reading skills at school [START_REF] Chae | Semantic/phonological error characteristics and reading fluency for school-aged children with language learning disabilities in 3rd to 4th Grades[END_REF]), clinical (e.g., to diagnose and monitor fluency disorders in children [START_REF] Eggers | Clinical Cases in Dysfluency[END_REF]), or scientific (e.g., to investigate the factors influencing the development of speech fluency during language acquisition [START_REF] De | Fluency in second language testing: Insights from different disciplines[END_REF]).

Fig. 1 .

 1 Fig. 1. Processing steps used to automatically segment speech signals into pseudo-syllables (transparent rectangles shown at step 3) and silent breaks (gray-shaded rectangles shown at step 3).

Fig. 2 .

 2 Fig. 2. Scatterplot relating average subjective ratings to automatic measures of speech fluency obtained with the MLR model, and associated regression line (R 2 = 0.94; RMSE = 0.34).

TABLE I PREDICTORS

 I USED IN THE LOW-LEVEL AND HIGHER-LEVEL SETS

Low-level set Higher-level set

	Speech ratio
	Rate of FBDS speech segments	Rate of pseudo-syllables
	Standard deviation of FBDS	Standard deviation of
	speech segment duration	pseudo-syllable duration
	Rate of FBDS silent segments	Rate of silent breaks

TABLE I INTRA

 I -RATER RELIABILITY (SPEARMAN'S RHO AND CRONBACH'S α)

	Rater	rho	α
	1	0.95 (p < 0.001)	0.98
	2	0.89 (p < 0.001)	0.96
	3	0.89 (p < 0.001)	0.95

TABLE II INTER

 II -RATER RELIABILITY: SPEARMAN CORRELATION COEFFICIENTS (RHOS) BETWEEN SPEECH FLUENCY RATINGS GIVEN BY EACH PAIR OF RATERS, AND ASSOCIATED ONE-TAILED P-VALUES

		Rater 2	Rater 3
	Rater 1	rho = 0.88	rho = 0.88
		(p < 0.001)	(p < 0.001)
	Rater 2		rho = 0.86
			(p < 0.001)

TABLE III AVERAGE

 III RATINGS (x̄) AND ASSOCIATED STANDARD DEVIATION (SD) FOR

		EACH RATER
	Rater	x̄	SD
	1	3.84	1.42
	2	3.66	1.36
	3	3.82	1.35

TABLE V AVERAGE

 V ROOT-MEAN-SQUARE ERROR (RMSE) AND ASSOCIATED STANDARD DEVIATION AS A FUNCTION OF PREDICTOR SET AND REGRESSION MODEL

	Predictor set	Regression Model	Average RMSE	Standard deviation
		MLR	0.46	0.11
	Low-level set	SVR	0.41	0.17
		RFR	0.36	0.12
		MLR	0.35	0.11
	Higher-level set	SVR	0.30	0.13
		RFR	0.37	0.12
				TABLE IV
				SPEARMAN'S CORRELATION COEFFICIENT (RHO) BETWEEN EACH PREDICTOR
				AND RATINGS OF SPEECH FLUENCY, AND ASSOCIATED ONE-TAILED P-VALUE
				Predictor	rho	p-value
				Rate of FBDS speech segments	0.85	< 0.001
				Rate of pseudo-syllables	0.88	< 0.001
				Standard deviation of FBDS speech segment duration	-0.60	< 0.001
				Standard deviation of pseudo-syllable duration	-0.37	0.001
				Rate of FBDS silent segments	-0.66	< 0.001
				Rate of silent breaks	-0.89	< 0.001
				Speech ratio	0.89	< 0.001

VI. ACKNOWLEDGMENTS

The authors are indebted to Prof. Mariko Kondo who helped with data collection, as well as to Dr. Julien Eychenne for his assistance with rater recruitment and his useful insights on Korean phonology. The authors also thank Dr. Saïd Jmel for his helpful statistical advices.