Jesus Gonzalez-Feliu

Josep-Maria Salanova Grau

Joëlle Morana

Evangelos Mitsakis

Urban logistics pooling viabililty analysis via a multicriteria multiactor method

Keywords: urban logistics, resource sharing, freight transport pooling, collaborative multicriteria analysis

Collaborative transportation and logistics pooling are relatively new concepts in research, but are very popular in practice. In the last years, collaborative transportation seems a good city logistics alternative to classical urban consolidation centres, but it is still in a development stage. This paper proposes a framework for urban logistics pooling ex-ante evaluation. This framework is developed with two purposes. The first is to generate comparable contrasted or progressive scenarios representing realistic situations; the second to simulate and assess them to make a "before-after" comparative analysis. In this framework, a demand generation model is combined with a route optimization algorithm to simulate the resulting routes of the proposed individual or collaborative distribution schemes assumed by each scenario. Then, several indicators can be obtained, mainly travelled distances, working times, road occupancy rates and operational monetary costs. To illustrate that framework, several scenarios for the urban area of Lyon (France) are simulated and discussed to illustrate the proposed framework possible applications.

Introduction

The freight transport industry is a major source of employment and supports the economic development of the country (Gonzalez-Feliu, 2012a). However, freight transport is also a disturbing activity, due to congestion and environmental nuisances, which negatively affects the quality of life, in particular in urban areas [START_REF] Crainic | Advanced Freight Transportation Systems for Congested Urban Areas[END_REF]. In this context, city logistics has been developed for more than fifteen years, giving solutions and methods to support public authorities and also other stakeholders in urban freight transport planning and management [START_REF] Taniguchi | City Logistics -Network modelling and Intelligent Transport Systems[END_REF]. Both the new trends in retail and commerce organisation and the technological innovation in supply chain and distribution planning have led decision makers to consider collaborative strategies to reduce overall cost of the supply process (Gonzalez-Feliu and Morana, 2012). This is the case of collaborative urban freight transport systems, where the different stakeholders of urban logistics can make collaborative agreements to improve the efficiency and then reduce the overall costs of the global supply chain activities networks. These schemas are commonly used in the transport field, mostly in logistics sharing [START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. In the last decade, several producers and/or transporters have elaborated mutual strategic plans which focus on a better usage of the transport vehicles by sharing them.

The aim of this paper is to present a method to evaluate the viability of logistics sharing, extending the analysis precedently made by [START_REF] Gonzalez-Feliu | Defining and evaluating collaborative urban freight distribution systems[END_REF]. First we present the main concepts of utban consolidation and collaborative freight transportation. Then, a methodology for evaluating such systems is proposed. This framework is organised in several modules, including a scenario simulator, a transportation management system (TMS), a risk management module and a multicriteria analysis method. In this paper we focus on the multi-criteria analysis tool. After describing the method, five scenarios are described and simulated. Finally, the main results are presented and commented.

Urban consolidation and logistics sharing

Traditionally, urban freight transportation planning has been made by the operating companies. In the last years, the public authorities have started to get involved into the development of solutions to deal with the major problems of freight transportation in city centres: congestion, air pollution, noise and other nuisances. Some of the most common measures taken by the authorities in different countries are restrictive policies, mainly regulation on parking, and street access. In several countries, surveys and data collection activities have been undertaken, and several studies give elements of freight transportation analysis and organisation for urban areas [START_REF] Ambrosini | Objectives, Methods and Results of Surveys Carried out in the Field of Urban Freight Transport: An International Comparison[END_REF]Allen et al., 2012a). These efforts are aimed at better understanding and quantifying these phenomena and represent a first step in the development of a new discipline, called by several authors City Logistics [START_REF] Taniguchi | City Logistics -Network modelling and Intelligent Transport Systems[END_REF] or Urban Logistics [START_REF] Anderson | Urban logistics -how can it meet policy makers' sustainability objectives?[END_REF]. The main goals of City Logistics measures and projects are related to congestion and air pollution rates, without a penalisation of the commercial activities in the city centres.

The most popular organizational scheme in urban logistics is that of urban consolidation centre (UCC), defined by [START_REF] Allen | Good Practice Guide on Urban Freight[END_REF] as "a logistics facility situated in relatively close proximity to the geographic area that it serves (be that a city centre, an entire town or a specific site such as a shopping centre), to which many logistics companies deliver goods destined for the area, from which consolidated deliveries are carried out within that area, in which a range of other value-added logistics and retail services can be provided". The first UCCs were private or semi-private initiatives, mainly developed for economic and optimization interests [START_REF] Dablanc | Les centres de distribution urbaine: un tableau comparatif[END_REF] and stopped when seen as less viable that other distribution systems. Later, environmental and social issues made public administrations to develop such systems for urban goods distribution [START_REF] Gonzalez-Feliu | Models and methods for the City Logistics -The Two-Echelon Vehicle Routing Problem[END_REF]. In order to make a European overview on UCC we have selected the main experiences shown in literature. We present in Table 1 a synthesis of the experiences taken into account.

We can observe that only few experiences are nowadays operating (about 1/3 of the total), and in many cases they need an important contribution of public authorities, both in terms of funding and organizational support. The country with a higher number of UCCs is the United Kingdom [START_REF] Allen | The Role of Urban Consolidation Centres in Sustainable Freight Transport[END_REF], which counts up to 13 operational platforms, i.e. about 40% of the total. Far from them on the total number of planned UCC but with higher success rates are Italy and France, the only two countries with more than half operational UCCs. The German UCC success rate is lower than the Italian and French ones (respectively 63% and 50%), but remains still higher than countries like The Netherlands (43%) or the United Kingdom (41%). This is because in Germany the public authority support did not result on operational logistics schemes, resulting on a very small number of still opened UCCs (about 20% of the total number of projects). Other countries have also projected UCCs in a minor intensity, but the results are not encouraging (if we add all the remaining experiences, we obtain a success rate less than 35%). Note than in both cases the UCC systems were supported by the public authorities, mainly in the form of strong regulation policies, but the financial support was lower than in France and Italy. As most of these experiences show, a UCC needs an important initial investment in terms of infrastructures, facilities and human and technical resources, including delivery vehicles, which are often compensated by public financial support. This support is not always enough, as operational costs are not always covered by the UCC overall income. Moreover, transport operators remain still reticent to use UCC under some conditions because the schemes related to these logistics platforms suppose at least an additional rupture of charge. The main limits to the usage of UCC can be grouped on the following categories [START_REF] Ville | The Limits of Public Policy Intervention in Urban Logistics: Lessons from Vicenza (Italy)[END_REF]: legislation, organization, cost and responsibility.

The main issue in UCC is to reach a rentability threshold that ensures the economic balance of the logistics facility. Imposing a unique UCC operator does not seem to be the most efficient solution, as shown by [START_REF] Ville | Los límites de las políticas públicas en el ámbito de la logística urbana: el caso de la ciudad de Vicenza[END_REF], and other strategies have to be found. Collaboration is one of the most promising areas of study in supply chain management and has started to be applied to freight transport management. Logistics sharing and logistics pooling are specific forms of resource sharing (Gonzalez-Feliu et al., 2010). Although in a narrow sense the word "sharing" refers to the joint or alternate use of inherently finite resources, both material and immaterial, it can also refer to the process of dividing and distributing [START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. Sharing resources in freight transport is related to three main issues: vehicle sharing, infrastructure sharing and route sharing. Concerning vehicle sharing, the logistics organisation is similar to that of car sharing or bike sharing systems for people transportation (Katzev, 2003, SUGAR project, 2010). Indeed, a freight vehicle sharing system proposes a fleet of shared vehicles, and each user of the system can book and use a vehicle for his or her own purposes. In these systems, each user continues to follow an individual organisation (vehicles are shared but each user continues doing its own transport schemes without merging them with those of other users). The second approach is that of platform sharing [START_REF] Gonzalez-Feliu | Simulation and optimisation methods for logistics pooling approachesin the outbound supply chain[END_REF], without necessarily a collaboration between users. These two issues have been recently studied in the literature (Simonot and Roure, 2007;Paché, 2008;Blanchard and Carbone, 2010;[START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. The third, less studied, is that of logistics pooling.

Logistics pooling can be defined by analogy to car pooling [START_REF] De La Morsanglière | La voiture en commun. Quelques données quantitatives sur l'agglomération lyonnaise[END_REF][START_REF] Gärlinga | Household choices of car-use reduction measures[END_REF]. We can introduce freight transport pooling as the mutual and contemporary use of a vehicle by two or more actors, all of them being conscious and having a direct action on decisions concerning this transport organisational aspects. Note than the usage of a freight forwarder or integrated logistics providers (4PL, LLP) are usual concepts in freight transport [START_REF] Ville | Los límites de las políticas públicas en el ámbito de la logística urbana: el caso de la ciudad de Vicenza[END_REF], but the responsibility and the decision making is relayed to a third party, who assumes its consequences. This is not a collaborative decision making case, as defined by Yearwood and Stranieri (2011). Indeed, in those transport and logistics schemes, the sender (or the receiver) contracts a company that organises all the transport and distribution related operations, involving other actors like transport operators and logistics providers. This company takes decisions and organises all the distribution processes, the sender (or the receiver) being only customers paying for a standard or personalised service. In logistics pooling approaches, the decisions are not taken by only one stakeholder but by the group participating on the pooling operations. As happens on car pooling [START_REF] De La Morsanglière | La voiture en commun. Quelques données quantitatives sur l'agglomération lyonnaise[END_REF], a freight transport pooling involves deliveries having a common trip chain in their overall path, and follows the same principles of multi-echelon transport with cross-docking (Gonzalez-Feliu, 2012b).

As logistics pooling follows similar schemes as integrated supply chains, we can envisage adapting methods from supply chain evaluation to estimate the effects of this form of collaboration in a sustainable development viewpoint. However, as several stakeholders are involved, current works do not represent the specificities of pooling strategies on logistics planning and optimization. In next section we will propose a methodology for analysisng the viability of logistics pooling for urban distribution, on the basis of a multi-criteria analysis for a set of different stakeholders.

Multi-stakeholder multi-criteria methodology

Although collaborative transportation is a promising approach, there are many limits and not all the stakeholders are a priori disposed to enter on such communities [START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. For this reason, public authorities and collaborative communities of practise want to develop decision support tools that help the individuals to consider the advantages and risks of collaboration globally, in order to take the decisions having a better knowledge of this field [START_REF] Thiengburanathum | An Integrated Planning-Simulation-Architecture Approach for Logistics Sharing Management: a case study in Northern Thailand and Southern China[END_REF]. In order to support public authorities and private actors in their choices concerning collaboration in urban freight transportation, we propose a framework based on modelling and simulation, that feeds a multi-criteria decision support method. From real urban distribution stakeholders, several data can be collected. From those data, a collaborative decision making multi-criteria analysis can be proposed to define the main indicators used to assist and support both public and private stakeholders' decisions.

The framework is organised as show on Figure 1.

Scenario construction

The scenario simulation is carried out adapting the method proposed by [START_REF] Gonzalez-Feliu | Defining and evaluating collaborative urban freight distribution systems[END_REF] to logistics pooling. The method works as follows. Consider a city, divided into representative zones. Each zone is defined by its population density, its commercial supply in terms of number of employees in each commercial activity class, its level of industry and distribution activity and its position with respect to the city centre. We define a set of commercial activities that will be involved on logistics pooling strategies, for example the grocery distribution sector. Using a delivery generation model [START_REF] Routhier | Du transport de marchandises en ville à la logistique urbaine, coll. 2001 Synthèses et Recherches n°59[END_REF]Toilier, 2007, 2010), the number of deliveries per establishment can be estimated. Then, from Henriot and Routhier's (2010) ratios, we assign a quantity of freight to each delivery [START_REF] Gonzalez-Feliu | Defining and evaluating collaborative urban freight distribution systems[END_REF]. The route construction procedure is described below. From Patier and Routhier's (2008) empiric results we define the involved transportation companies. These companies are defined by a heterogeneous fleet of vehicles, a set of depots and a set of possible cross-docking platforms. Then, using Gonzalez-Feliu and Salanova's (2011) scenario simulator, we generate the vehicle routing problems that will be solved using the route construction procedure.

Scenarios

Route construction (TMS)

Criteria definition and characterization

Multicriteria mapping Individual multicriteria analysis Systemic multicriteria analysis

Transportation Management System (TMS)

The transportation management system is a tool that builds the routes for each transportation carrier involved in the collaborative transportation system and estimates the travelled distances and the environmental impacts, in terms of emissions of greenhouse gases. This module uses an algorithm for the two-echelon vehicle routing problem [START_REF] Gonzalez-Feliu | Models and methods for the City Logistics -The Two-Echelon Vehicle Routing Problem[END_REF], 2012a) adapted to collaborative transportation systems.

The algorithm is a fast heuristics, following a two-step procedure. This has been chosen because the methodology is aimed to support tactical decisions of collaborative communities, so the driving route estimation method has to be assimilated to a commercial route planning tool, in order to be use don real-size cases in a few seconds. First, a sweep algorithm is used in order to make clusters of customers, assigning them to a fist-level route and not to a satellite. When dealing with direct shipping, a classical sweep algorithm as those developed for CVRP applications is applied. For 2E-VRP, the sweep algorithm takes into account the satellite position in order to define the routes (i.e., customers are assigned to a 1st echelon vehicle but taking into account the satellite this vehicle will bring the freight for a cross-docking operation.

Second, the routes are built using a semi-greedy algorithm (Gonzalez-Feliu et al., 2010). There are two types of routes: the 1st-echelon ones go from the depot to the satellites, and the 2nd-echelon ones from the satellites to the customers. The routing phase is common for all the configurations: once the customers are grouped, a semi-greedy is applied in order to build the 2nd-echelon routes. Then, the 1st-echelon ones are obtained easily by a combinatorial procedure, since the number of possibilities is very small. Note that in some cases the instances solved are related to heterogeneous fleets. This issue has been taken into account in the route construction. Finally, a small postoptimisation procedure using fast local search techniques is used.

This method has been developed to simulate reallistic routes, not to make a nearoptimum theoretical optimization. This choice is motivated by the works of Ackoff (which main contributions are seen in [START_REF] Ackoff | Optimization + objectivity = opt out[END_REF]Ackoff, , 1978ª and 1978b)), who shows the distancement of operations reseach from real applications and the strong object reduction (in the sense of [START_REF] Bonnafous | Le siècle des tenèbres de l'économie[END_REF] which limítate most of current Works of operation research to theoretical and conceptual analyses without being able to discernate practical implications. Opposingly to those works, the proposed algorithm is of simple understanding and usage and is close to the current delivery practices according to Ambrosini and Routhier's (2004) findings.

Criteria definition

The criteria that will feed the multi-criteria analysis are obtained from three modules: the TMS (already described), by converting the estimated distances into economic indicators, the environmental module, and the risk management module, to obtain respectively environmental indicators and risk factors.

Economic indicators

Economic indicators are related to two main variables. First is the total travelled distances, that can be divided into two categories: the in-route distance and the access distance. The first distance is the distance travelled from the first customer/satellite until the last one (passing by all the customers/satellites in the defined order); the second distance is the distance travelled from the depot/satellite to the first route point and from the last route point back to the depot/satellite. The calculation of the in-route travelled distances is done by the Euclidian distance between each two consecutive route points, excluding the depot/satellite from the route. The calculation of the access distance is done by the Euclidian distance between the depot/satellite and first and last route points. The cost of the routes are fixed costs (driver basically) and variable costs (length, time, consume and contamination). To estimate the travel time, average speeds are used, with higher values for the access to the route, and lower values for the in-route travel. From these distances, we can estimate the yearly quantity of fuel needed. Second is the total working time, which is composed of total travel time, the time spent for loading and unloading purposes and the driver's pauses imposed by the current legislation. To estimate these times, we define a mean speed between each O/D pair, following the considerations of [START_REF] Routhier | Du transport de marchandises en ville à la logistique urbaine, coll. 2001 Synthèses et Recherches n°59[END_REF] and Presstalis (2011). The travel times are then estimated from the travelled distances. Then, the total work time is calculated associating a loading or unloading time to each pick up or delivery (related to the quantity of freight to pick up or delivery). If a route in terms of travel time does not exceed the legislation working times, we add consequently the corresponding pauses of the driver. If these constraints are not respected, the route is re-optimised, even split, to meet the legislation. From those working times, we can estimate the total number of workers and their annual cost in terms of salary.

Then we calculate the yearly cost by converting the distance travelled of each route to a monetary cost. To do this, we take into account the conversion tables proposed by Generalitat de Catalunya (2011), including fuel costs, maintenance and vehicle insurance (related to the distance travelled) and crew costs, related to travel hours, estimated from the distances travelled and average urban speeds [START_REF] Routhier | Du transport de marchandises en ville à la logistique urbaine, coll. 2001 Synthèses et Recherches n°59[END_REF].

Environmental indicators

Concerning environmental indicators, several possibilities can be taken int account. Because in this work we aim to study the effect of organisational strategies having the same technological support, pollution emissions of the proposed scenarios are only dependent on the trealled distances and the type of vehicles that can be used, which do not vary from one scenario to the other (the variation will be on the usage rates of these vehicles, but the overall set of possible vehicles remain the same in all scenarios). For this reason, we propose an analysis on only traffic issues. From the travelled distances per truck, we can easily estimate travel road occupancy rates. To do this, each truck distance is weighted by a coefficient depending on its weight, as stated in Table 2: Another indicator that can be calculated is the total number of parking hours in congested city areas. This indicator is directly related to the total loading and unloading time in central areas. In order to take into account the vehicle's surface, we have pondered the total number of hours by the type of vehicle (i.e. weights presented in table 1), to obtain the total number of h.PCU.

Risk factor identification

The risk management module is presented in [START_REF] Gonzalez-Feliu | Defining and evaluating collaborative urban freight distribution systems[END_REF], from the works of [START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. It follows the following schema: Considering the technologies, tools and their usage levels, several choices must be made in order to set up the best solution of logistic sharing services. In order to make these choices, it is important to formulate questions related to the goals and the risks of the project, and to find the appropriate answers. Accordingly, it is important to make a deep analysis of the possible risks that the project may encounter. From the work of [START_REF] Seiersen | Systèmes d'information logistique[END_REF], we can list the main categories of risks related to a collaborative urban logistics pooling project.First are the risks related to the urbal logistics sharing project accounting itself, more precisely to the different type of resources that can be affected by the project. They refer to financial, economical, technical, technological or human aspects, and they deal either with both individual internal resources made available to the entier community or common new resources adquired for the overall group of collaborators. In general, each partner of the logistics pooling community knows its implications in financial and accounting terms, but those decisions are taken at two different levels: first individually for each stakeholder then a consensus has to be searched [START_REF] Raifa | Negociation analysis. The science and art of collaborative decision making[END_REF]. Then, risks related to the organization of the project and its continuity have to be taken into account. Two types of risks can be identified in this Individual strategic decisions Sharing management category [START_REF] Seiersen | Systèmes d'information logistique[END_REF]: those related to operational decisions, which are in general individually made, and those related to tactics and strategies, which need both individual and collective decisions taken by different decisions makers, even at each stakeholder's side. In this context misunderstandings and other obstacles to collaboration have to be seen as potential risks.

The technological risks are in general related to functionality, robustness and compatibility. According to [START_REF] Seiersen | Systèmes d'information logistique[END_REF], before choosing a technology, it is important to think about these questions. Risks related to policies, processes and current practices appear when development and usage of new logistics solutions need significant changes on people's thoughts and actions to become operational. Continuous social analysis during all conception and development phases are crucial for strongly innovative solutions to make them stable and continuously deployed [START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. After that, risks related to the impact of the systems in the current and future operations seem another important group, at both human and technical levels. We find in this category the collaboration rules and the respect of them [START_REF] Gonzalez-Feliu | Collaborative transportation sharing: from theory to practice via a case study from France[END_REF]. When adopting an information system of a new technology, the dependence risks are not negligible. If the new logistics system is based on several technologies, the risks related to the dysfunction of these technologies need to be considered. When a technological tool presents a dysfunction, the system can be less efficient, or can stop because of it [START_REF] Seiersen | Systèmes d'information logistique[END_REF]. Last but not least, the juridical risks: as seen in [START_REF] Ville | Los límites de las políticas públicas en el ámbito de la logística urbana: el caso de la ciudad de Vicenza[END_REF] it is important to take into account the juridical consequences of public policy and the changes this policy implies on the urban organizations.

Multicriteria mapping

Once the criteria are defined, we can extract them and make a multi-criteria analysis. Note that the multi-criteria method is developed to choose among a set of solutions. For this reason, different strategies are presented and simulated first separately for each stakeholder and then for the overall group. For each strategy, the TMS and the environmental modules are used in order to estimate the main costs and environmental issues. Moreover, we feed the risk management module with all the scenarios, in order to give the risk factor for each of them. In this simulation we analyse the case of an individual decision maker, i.e. one of the five transportation carriers, who wants to enter on a collaborative transportation system and needs a decision help. From this consideration, we propose the following criteria for our analysis, each of them measured by an indicator:

• Economic costs: Transportation cost (in €) • Travel distances: Travel distances (in km) • Contribution to traffic: Road occupancy rates (in km.PCU) • Contribution to greenhouse gas (GHG) emissions: Individual GHG emissions (in CO2-equivalent units) • Risks: Risk factor (1 to 5)

After estimating the indicators corresponding to each criterion, a graph is made, first for each stakeholder then for the overal group. Each criterion has been defined in its specific and own scale; for that reason a graohical representation that harmonises all scales is made to make the multicriteria mapping presentation easier to read and understand. Moreover, the criteria are graphically positioned consecutively in order to observe them one by one without crossing them. Then they are presented consecutively in the same graph, i.e., they have to be read independently because there is not a relation of scale between the criteria.

An example of application

In this section we propose a set of scenarios that will be used to illustrate the proposed framework. In our example we supposed 5 different operators (representing each a realistic transportation carrier). Each operator has a depot, a few satellites for consolidating the cargo, and its own fleet of trucks, with two different sizes of trucks. The total number of customers is 408, and the number of satellites 12. Moreover, each customer can be served by more than one transportation carrier. In order to propose a realistic set of scenarios, we used a real urban network: that of Lyon (France), the second largest in France, only after Paris Metropolitan Region.

In 2006, this area consisted of about 2.000.000 inhabitants and 800.000 households. We use a database that derives from the 2006 household trip survey of Lyon urban area (Grand Lyon, 2006). This file contains several databases from which we can extract information related to the population and the demography, as well as to define the main retailing zones of the urban area (the overall surveyed territory has been divided into a set of about 750 zones). The information related to the retailing activities (number of employees, dimension, and type of establishment) are extracted from a SIRENE file, the establishment censorial database of the French Institute of Statistics (INSEE). We took the SIRENE file of the year 2005 in order to have results from a similar time period. From the SIRENE file we extracted the data corresponding to the small grocery retailers of Lyon and Villeurbanne (about 400 establishments). These retailers will be the final destinations of the freight to be delivered and are grocery retailers with a total surface lower than 400 m². Then, using FRETURB, we estimate a weekly number of deliveries per retailer. Then we define 12 cross-docking platforms located in the near periphery of city, mainly in industrial zones. The industrial zones are extracted from the SIRENE file. We define the logistics facilities from the SIRENE file and locate them in the near periphery of city, mainly in industrial zones. The depots are located in the peri-urban area, also known as the far periphery of the city. Then, a quantity of freight is associated to each delivery (as seen on [START_REF] Gonzalez-Feliu | Defining and evaluating collaborative urban freight distribution systems[END_REF]. We then estimate each company's transport plan using an adaptation of Gonzalez-Feliu and Salanova's (2011) fast heuristic algorithm for two-echelon transport optimization in urban areas, according to a two-step procedure. First, a non-hierarchical clustering method allows assigning customers to a vehicle. Second, the routes are built using a semi-greedy algorithm (Gonzalez-Feliu and Salanova, 2011). We simulate a total of 5 scenarios, described below:

1. A non-collaborative strategy where only the big trucks are used, visiting a large number of clients due to the bigger capacity of the vehicles. 2. A second non-collaborative strategy where the big trucks are used for distributing the cargo to consolidation facilities, and from there to the final clients using the smaller trucks. 3. A partial transportation pooling network where 2 operators are collaborating, while the other are acting as in the second scenario. The two collaborating operators share their consolidation platforms, and consolidate cargo destined to the same clients, sharing also their fleets of small trucks. 4. A collaborative transportation pooling network where all the operators are collaborating, using all the consolidation platforms for consolidating the cargo and sharing their fleet of small trucks.

In the following we propose the results of the multicriteria analysis. We observe from the analysis that finding a solution that satifsfies each stakeholder is not evident. Indeed, although for operators 2 and 4 it seems that scenario 3 is the most suitable, for the other operators it is not easy to find, since each scenario is better tan others in one or more criteria but is dominated in other ones. If we pay attention to the overall system, The logistics sharing approach (scenario 4 is not the best solution, since it represents the higher risk and is overperformed by the others in terms of congestion and pollution (that can be explained by the fact that the small gains on some operators are compensated by big loses on others, due to the fact that each stakeholder presents optimized distribution schemes). On the other hand, it presents the overall lower costs and driving times. Opposignly results are observed for scenario 1 (the reference). Scenarios 2 and 3 remain as middle choices, but can be in this case the most suitable, since they are closer to the best solution for each criteria (they are ranked 2nd and 3rd in all criteria).

Conclusion

In this paper we have presented the main issues in planning urban freight transportation pooling systems. Moreover, a multi-stakeholder multi-criteria decisión support method for collaborative transportation decision support is presented, more precisely to help the urban goods movement decision makers in their strategic choices (for both public and private stakeholders). The proposed method combines several modules. From the simulation, we observe that collaboration is not evident, and it can lead to cost reduction at some conditions. Moreover, it is the most environmental-friendly solution from those taken into account. However, implementing these systems presents risks that have to be evaluated. From the analysis we observe that although collaboration is an interesting field, it is early to say if it will be well applied to city logistics. For this reason we need to develop decision support systems and sensibilise the public and the private stakeholders in order to find global city logistics solutions in an urban-system point of view.

Figure 1 :

 1 Figure 1: Chart of the strategic evaluation

Figure 2 :

 2 Figure 2: Strategic decisions and risk schema for logistics sharing (adapted from Gonzalez-Feliu and Morana, 2011)

Figure 3 .Figure 4 .Figure 5 .Figure 6 .Figure 7 .Figure 8 .

 345678 Figure 3. Multicriteria results for operator 1

Table 1 : Synthesis of the main European experiences

 1

Table 2 : Weight factor for traffic issues (adapted from Routhier et al., 2009)

 2

	Total on-load weight	Weight factor
	Less than 3.5 t	1
	3.5-7 t	1.5
	7-16 t	2
	More than 12 t	2.5

Acknowledgements

Part of this research has been financed by the ANR (French National Research Agency) MODUM (Mutualization and optimization of urban goods trips). The authors also like to thank Teodor Gabriel Crainic, From CIRRELT, Canada, Tai-Yu Ma and Mathieu Gardrat from Laboratoire d'Economie des Transports, France, and Achim Puetz from Universitat Jaume I, Spain, for their valuable suggestions to improve the present paper.


Stefan Ziegler

Bogdan Ludusan

Guillaume Gravier

TOWARDS A NEW SPEECH EVENT DETECTION APPROACH FOR LANDMARK-BASED SPEECH RECOGNITION

Keywords:

In this work, we present a new approach for the classification and detection of speech units for the use in landmark or eventbased speech recognition systems. We use segmentation to model any time-variable speech unit by a fixed-dimensional observation vector, in order to train a committee of boosted decision stumps on labeled training data. Given an unknown speech signal, the presence of a desired speech unit is estimated by searching for each time frame the corresponding segment, that provides the maximum classification score. This approach improves the accuracy of a phoneme classification task by 1.7%, compared to classification using HMMs. Applying this approach to the detection of broad phonetic landmarks inside a landmark-driven HMM-based speech recognizer significantly improves speech recognition.

INTRODUCTION

In state-of-the-art hidden Markov model-based (HMM) automatic speech recognition (ASR), speech is modeled as a sequence of phone-segments, often referred to as the beadson-a-string model of speech [START_REF] Ostendorf | Moving beyond the beads-on-a-string model of speech[END_REF]. In contrast to that, acousticphonetic or event-based approaches to speech recognition model speech as a stream of asynchronous phonetic events, which have to be further processed to obtain a higher level speech representation [START_REF] Stevens | Toward a model for lexical access based on acoustic landmarks and distinctive features[END_REF][START_REF] Jansen | Point process models for event-based speech recognition[END_REF][START_REF] Juneja | Speech recognition based on phonetic features and acoustic landmarks[END_REF][START_REF] Glass | A probabilistic framework for segmentbased speech recognition[END_REF]. Many of these approaches require the detection of phonetic events as time instances, referred to as landmarks.

There are numerous studies concerning the detection of phonetic events in the speech signal, which can be divided into two general groups. The first group uses expert knowledge to derive detection rules from various signal representations (e.g., [START_REF] Stevens | Toward a model for lexical access based on acoustic landmarks and distinctive features[END_REF]). The second group uses a classification and detection approach, where labeled data is converted into the desired phonetic feature representation and classifiers are trained to map acoustic observations onto phonetic speech This work was partially supported by the Agence National de la Recherche in the framework of the ASH project. Furthermore, we would like to thank Christian Raymond for advices concerning the use of his boosting software.

units (e.g., [START_REF] Jansen | Point process models for event-based speech recognition[END_REF][START_REF] Juneja | Speech recognition based on phonetic features and acoustic landmarks[END_REF]). Employing these classifiers sequentially on the speech signal results in detection functions indicating the presence of the desired speech units. These classifiers operate usually on a frame basis, extracting a fixed-dimensional observation vector for each frame, eventually considering a small context window. On the one hand, this fixed observation space enables the use of non-linear and non-parametric classifiers like support vector machines [START_REF] Jansen | Point process models for event-based speech recognition[END_REF][START_REF] Juneja | Speech recognition based on phonetic features and acoustic landmarks[END_REF] or multilayer perceptrons for classification, which possess good discrimination abilities and can be trained efficiently. On the other hand, predicting speech units by observing a fixed-size fraction of the speech signal does only partly model time-variable speech units, including phonemes and many articulatory feature units.

We are interested in the problem of extracting a fixeddimensional observation vector from time-variable speech units, to discriminate them using a classifier that requires a fixed number of observations. To obtain this observation vector, we use a maximum-likelihood segmentation method, to force each labeled speech unit into three spectrally homogeneous parts from which the observation vector is extracted. In our work, this observation vector is used to train speech units with boosted decision trees and to predict unknown segments during testing. To obtain a detection function, indicating the locations of a speech event by its local maxima, we search for each frame the segment that maximizes the classification score at this frame. This method is applied to a recently proposed landmark-driven ASR framework, that uses phonetic landmarks to guide the search in an HMM-based ASR system [START_REF] Ziegler | Using broad phonetic classes to guide search in automatic speech recognition[END_REF].

The paper is organized as follows. First, we present the details of our proposed method and review boosting with an ensemble of weak learners, as well as landmark-driven ASR. Evaluation is carried out on a phoneme classification task and on landmark-driven speech recognition experiments and we conclude with an outlook on future work.

PROPOSED SYSTEM FOR SPEECH EVENT DETECTION

In event-based speech recognition, phonetic speech units are often trained and classified at the frame level. The classification scores of successively predicted frames correspond to a detection function, that indicates the presence of the speech event, usually by its magnitude. Landmarks, corresponding to single time instances marking the most salient points of speech events, are then obtained by post-processing the detection profiles, varying from simple peak-picking to processing with statistical models (e.g. [START_REF] Jansen | Point process models for event-based speech recognition[END_REF]). To capture spectral transitions and temporal information, usually a concatenation of parametrized speech frames create a fixed-dimensional observation space for each frame for classification. But since speech units are commonly modeled as time-variable segments, a fixed number of frames will either capture only fractions of the spectral information of the speech unit or contain misleading information about previous or following speech events. If the detection function is obtained by inaccurate modeling of the desired speech event, it is difficult to recover the loss in information by post-processing noisy and erroneous detection profiles.

In the following, we present a method that overcomes this shortcomings by extracting a fixed-dimensional observation vector x from a given time-variable sequence of parametrized speech Y = y 1 , . . . , y t (section 2.1), corresponding to any desired speech unit. We are able to keep a fixed-dimensional observation space, since we reduce every speech segment to three subsegments, similar to a three-state HMM. This observation vector enables the use of any desired classification method, while generalizing all time-variable speech units. In our case, we apply a committee of boosted decision stumps to train a classier F (x) (section 2.2). F (x) is then used to generate a detection function d k = d k,1 , . . . , d k,t for k desired speech units, with the profile of d k indicating the presence of this speech unit in the unknown signal. For each k-th detection function, a set of l k time instances L k = {t 1 , . . . , t l k } is extracted, representing the exact locations of the speech event associated with speech unit k (section 2.3).

Maximum-likelihood segmentation applied to speech units

In the following, we apply the maximum-likelihood speech segmentation approach described in [START_REF] Saijayram | Robust parameters for automatic segmentation of speech[END_REF] to the segmentation of speech units. Given the parametrized frame-based representation of a labeled speech unit Y = y 1 , . . . , y n , for example a sequence of Mel-frequency cepstral coefficients (MFCCs) vectors, where n corresponds to the length in frames of the speech unit, we aim at finding the segment borders b 2 and b 3 which segment the unit into i = 3 segments (y b1 , . . . , y b2-1 ), (y b2 , . . . , y b3-1 ) and (y b3 , . . . , y n ), with b 1 = 1.

The optimal segment borders are considered to be the borders minimizing the intra-segment distortion of each segment. As proposed in [START_REF] Saijayram | Robust parameters for automatic segmentation of speech[END_REF], we measure the intra-segment distortion as the accumulation of distances from each frame inside the segment to the segment-centroid µ i . Using the euclidean distance between frames as a distance measure, the task is to find the two frames b 2 and b 3 given b 1 = 1 and b

4 = n + 1 according to b 2 , b 3 = arg b2,b3 min 3 i=1 bi+1-1 n=bi y n -µ i ; b 1 = 1, b 4 = n+1.
(1) This corresponds to a shortest-path problem, which can be solved for each segment using dynamic programming.

The observation vector x for each speech unit is obtained by first concatenating the centroids of the obtained segments µ T 1 , µ T 2 , µ T 3 . Additional attributes added to x are the length of each segment, to capture temporal information, and the three intra-segment distortion measures bi+1-1 n=bi y n -µ i to measure the homogeneity of each segment.

Boosting for speech unit classification

Having extracted a fixed-dimensional observation vector for each variable-length speech unit, speech unit models can be trained using the desired classifier. In this work, we boost decision stumps with the multiclass AdaBoost.MH algorithm [START_REF] Freund | A short introduction to boosting[END_REF].

Boosting is an ensemble learning method that iteratively learns weak learners h m (x) to classify instances, that were for the most part misclassified by the previous learner. The error of learner h m (x) determines its weight α m . The outputs of m weak learners h(x) are combined to a strong classifier H(x), providing a score for every class k with

H(x) = m α m h m (x).
A set of weak learners provides a very flexible framework during prediction, since the trade-off between computation time and precision can be adapted by changing the number of weak learners. Furthermore, the use of decision trees allows to insert nominal attributes in the future, like information about gender or bandwidth.

The training data used in this work includes over 200 hours of speech, or about 8.5 million labeled speech units. Training on the entire data set is usually not feasible, since most algorithms need the data to fit into memory and training time increases non-linearly. We follow the approach proposed in [START_REF] Chawla | Distributed learning with bagging-like performance[END_REF], which is to divide our training set into J partitions and to train a classifier H j (x) on each partition. The final classification consists in averaging the output scores of the full committee of classifiers:

F (x) = J j=1 H j (x) J .
(2)

From classification to detection

As stated in the introduction of section 2, F (x) is used to obtain a detection function

d k = d k,1 , . . . , d k,t . If F (x)
is trained to predict single frames y t of parametrized speech, d k,t directly corresponds to the prediction score of class k at frame t. Since our classifier provides segment and not framebased prediction scores, we provide a new method to generate detection profiles from a collection of predicted segments. Intuitively, in order to obtain d k,t , one has to search for the segment containing t, that maximizes the likelihood of the given speech unit at frame t. Given a collection of overlapping segments, with each segment corresponding to a hypothetical speech unit, we can directly compare the score of variable-length segments, using our proposed observationvector. A collection of segments does not correspond to a graph, so that single segments might not have a connection to preceding or subsequent segments (see simplified example in Figure 2). With this work being a preliminary study, we do not consider the task of finding a collection of suitable prior segments, which is desirable to reduce the computational costs for generating d k,t . Instead, we use an exhaustive collection of segments by predicting the observations extracted from all possible segments inside the speech signal, up to a maximum segment length of 300ms. Using fast classification like boosting, and an effective implementation for the solution of equation 1, this stays computationally feasible. The value of the detection function d k,t at t is calculated as follows:

d k,t = max F k (x (s,e) ); s ≤ t ≤ e, e -s ≥ i -1. (3)
F k (x) is the score of the k-th detection function given the observation x. s and e correspond to the first and last frame of a segment and x (s,e) to the observation vector extracted from this segment. The minimum number of frames in a segment is i = 3.

The obtained detection function indicates the positions of the k-th speech event in the speech signal by its local maxima. Since we put most effort into the accurate modeling of the speech units, we expect clear indications of the speech events, without requiring sophisticated post-processing. The following simple peak picking algorithm can be applied to a system of discrete speech units, like phonemes or broad phonetic classes (BPCs). Given the detection functions for k speech units, we extract landmarks in three steps:

1. We first collect the time instances corresponding to local maxima for each detection function d k and associate each local maximum t with its magnitude d k,t . Since a local maximum corresponds to a segment of at least three frames, the central frame t of a segment is always selected as the time instance t of the local maximum.

2. If several units k share the same local maximum t, only the landmark with the highest magnitude d k,t is kept at t.

3. Some of the remaining landmarks might correspond to a local maximum at a very low magnitude. Therefore, at each landmark, all units k with a score d k,t bigger than the landmark magnitude are also activated at that time instance. Afterwards, each set L k = {t 1 , . . . , t l k } contains the final collection of l k discrete time instances signaling the presence of the k-th speech event.

If a local maximum t of speech unit k already corresponds to the highest score d k,t at that time frame, step 3 will have no effect. If not, this step will merge several speech units into broader units, effectively signaling the possible presence of several units at the same time.

LANDMARK-DRIVEN HMM-BASED ASR

We use the presented speech event detection approach to extract broad phonetic landmarks for a recently proposed landmark-driven HMM-based ASR framework [START_REF] Ziegler | Using broad phonetic classes to guide search in automatic speech recognition[END_REF]. In this framework, broad phonetic landmarks are used to guide the Viterbi decoding of the first pass of a HMM-based speech recognizer, which we briefly summarize in the following.

The Viterbi algorithm searches the best path reaching state (j, t) by computing a score S(j, t) = max i S(i, t-1)+log(a ij )+log(p((y) t |j))+R(j, t), [START_REF] Juneja | Speech recognition based on phonetic features and acoustic landmarks[END_REF] where log(a ij ) is the transition probability and log(p(y t |j)) corresponds to the acoustic likelihood. R (j, t) enhances paths through states j that are compatible with phonetic landmarks by R (j, t) = λ j,t • R max . With states corresponding to triphones, checking the compatibility is trivial, since each state can be directly linked to one BPC. λ j,t is a binary indicator, that becomes 1 if state j is compatible with the landmark at time t and 0 if there is no landmark at all. R max limits the influence of landmarks onto the overall score and has to be determined experimentally.

EXPERIMENTS

Our experiments focus on two objectives. First, we are interested in evaluating whether our proposed approach can capture the acoustic information of speech units and compare the classification performance to existing approaches. Second, the proposed approach will be used to extract broad phonetic landmarks to guide the search of an HMM-ASR system as described in section 3.

Experimental setup

The speech corpus used in the experiments corresponds to radio broadcast news in the French language from the ESTER2 broadcast transcription evaluation campaign [START_REF] Galliano | The ES-TER 2 evaluation campaign for the rich transcription of French broadcasts[END_REF]. Our training set consists of over 200 hours of speech, the development set of 3 hours and the test set of 4.5 hours from 4 broadcast shows (radio Africa1, radio Inter, radio RFI, radio TVME). The test set is divided into J = 12 non-overlapping partitions for a committee of 12 classifiers. Classification experiments are run on the development set, while speech recognition is carried out on the test set. Speech labels are obtained by forced-alignment.

The speech recognizer used for recognition experiments is a two-pass system, with the first pass generating a word graph and the second pass rescoring the previously obtained graph using more complex models. We use the tool bonzaiboost 1 for the training of the ensemble of weak classifiers .

Phoneme classification

While landmark detection is primarily used for the detection of phonetic events, our proposed method is designed to learn any time-variable speech unit. This includes phonetically motivated units, as well as phonemes. We decided to do classification experiments on a phoneme classification task, since this is a challenging task, considering the fine acoustic distinctions between phonemes. Our French phoneme alphabet consists of 40 phonemes, including 5 non-speech events.

HMM-monophone models serve as the baseline phoneme classifier, because of their time warping ability, which makes them perfectly suitable for discriminating time varying speech units. The models employed correspond to monophone 3state left-to-right HMMs with 64 diagonal-covariance Gaussian components per state. HMMs are trained on the full 1 developed by Christian Raymond, available at http://bonzaiboost.gforge.inria.fr Fig. 4. Proposed observation vector for the phoneme classification experiments. On the left, the observation vector extracted for each phoneme as proposed in this paper (classifier 2, 3, 4 and 6). On the right, the observation vector as a concatenation of subsequent speech frames at the center of each phoneme (classifier 1). HMM (64 gaussians) 65.9 6 committee, proposed (J = 12, depth=2) 67.6

Table 1. Classifiers of the phoneme classification task. The classifier is described by its classification method (e.g. boosting) and observation vector (e.g. proposed).

training set using the same speech parametrization as the boosted ensembles, which are 39-dimensional MFCC vectors, composed of 13 MFCC coefficients with first and second order derivations. To compare the abilities of the classifiers to capture the acoustic properties of each phoneme, we evaluate on a pure classification task, i.e. all observation vectors were extracted using the known phoneme borders obtained by forced alignment, either for training or predicting the associated phoneme. HMM-classification was performed by force-aligning each model to the known phoneme borders and comparing the obtained likelihoods. First, we trained four classifiers on only one partition (i.e. 1/12-th) of the training data for comparison, before creating the full committee of 12 classifiers. The first classifier uses the three concatenated frames at the temporal center of each phoneme as observation vector x for training and testing (see Figure 4). Training and predicting only the center of each phoneme is supposed to minimize errors due to coarticulation effects. We compare this approach to our proposed method, which we run using weak learners of different depth (classi-Fig. 3. Detection profiles of the four broad phonetic units vowels, glides, plosives (voiced) and fricatives (voiced) for the french word aujourd'hui (uttered during a broadcast news show) and its corresponding broad phonetic annotation. On the left, every frame of the detection function was predicted using classifier 1 (see Table 1). On the right, the proposed method was used to generate the detection score at each frame (using classifier 3). While vowels, plosive and fricative are well indicated for both classifiers, the detection function for classifier 1 is more noisy, especially for the vowels. Glides seem to be more clearly represented on the right side, which can be due to the fact, that glides correspond to slow articulatory movements, which can be difficult to capture by concatenated frames. fier 2, 3 and 4). For all experiments the number of boosting rounds was limited to 3000. Table 1 displays the classification accuracy on the development set as the percentage of correctly classified phonemes. Using the proposed observation vector for boosting increases the performance by 7.3% (using decision trees with depth=2) compared to training on concatenated frames. While boosting on one partition of the training set does not outperform the accuracy of HMMs, applying the committee of 12 boosted weak ensembles improves by 1.7% compared to HMMs.

Landmark-driven ASR

To use our phoneme classifier for broad phonetic landmark detection, we simply derived a collection of detection functions d j for j = 7 BPCs vowels, nasals, glides, fricatives (voiced and unvoiced) and plosives (voiced and unvoiced) by scoring every frame d j,t with the score d k,t of the phoneme k, tection profiles obtained by predicting single frames using classifier 1 and by employing our proposed method (classifier 3). Table 2 contains information about the landmark accuracy.

classifier
A phoneme error corresponds to a phoneme with at least one landmark that misclassifies this phoneme. As one can expect, directly extracting local maxima from frame based detection functions leads to more than twice as much landmarks compared to the proposed method, because of the noisy detection profile (see also Figure 3), resulting in many incorrect landmarks. It should be noted, that this is partly due to our landmark extraction algorithm, which is designed to avoid any post processing, like smoothing or heuristic peak-picking algorithms. The high average number of active phonemes at each landmark is due to many landmarks that consist of several merged BPCs.

For the landmark-driven ASR experiments according to section 3, we used the landmarks obtained with classifier 6. First, the development set was used to tune R max (see equation 4). The optimal R max was then employed for the landmark-driven decoding on the test set. The results in Table 3 show an improvement for all 4 broadcast shows tested, compared to the baseline which does not include landmarks. The improvement in word-error-rate (WER) varies from 0.1 (radio Inter) to 0.7 (radio Africa1). The overall WER of the test set was 23.1%, compared to a 23.5% baseline, and a Wilcoxon signed rank showed it to be significant at the 5% level. Compared to the WER obtained by the phonetically guided decoding presented in [START_REF] Ziegler | Using broad phonetic classes to guide search in automatic speech recognition[END_REF], where BPCs were trained on selected frames and decoding was guided by predicting the BPC of every frame, no broadcast show of the test set was degraded by the use of broad phonetic information.

CONCLUSIONS

In this work, we presented a new method for the detection of variable-length speech units. We used segmentation to obtain a fixed-dimensional observation vector for each speech unit to train a committee of boosted decision stumps for speech unit classification. To detect speech units in an unknown signal, we searched for each time frame the corresponding segment, that provides the maximum classification score for the desired speech unit. This approach improved the accuracy of a phoneme classification task compared to HMM-phoneme classification, as well as the WER of a hybrid HMM-based landmark-driven ASR framework, compared to its HMMbased baseline.

With the promising results we were able to obtain by employing our proposed framework, there are several directions for future research. First, the proposed fixed-dimensional observation space could be refined, by considering different segmentation and attribute-extraction strategies. Second, the step from classification to detection by evaluating all possible segments should be replaced by an efficient search for a suitable collection of prior segments. While boosting decision stumps performed well on the presented classification task, applying other classification techniques might further improve speech unit classification and detection.

Fig. 1 .

 1 Fig. 1. Diagram of the proposed method of using segmentation to extract a fixed-dimensional observation vector from a labeled speech unit (training) or an unknown segment (testing).

Fig. 2 .

 2 Fig. 2. Simplified example illustrating the extraction of a detection function d k from a given set of segments. Each segment is displayed with its prediction score. The maximum scores at each frame t are mapped onto d k,t .

Table 2 .

 2 Statistics of extracted landmarks on the whole development set for three classifiers. AVG BPC size corresponds to the average number of phonemes provided at each landmark.

	1	3	6

Table 1 .

 1 For classifier 1 we equally extracted landmarks, but using the detection function obtained by predicting each individual frame. Figure3compares de-

	broadcast WER baseline WER landmark-driven
	Inter	18.7	18.6
	RFI	17.6	17.3
	TVME	24.2	23.8
	Africa1	31.5	30.8

Table 3 .

 3 Speech recognition performance driven by broad phonetic landmarks. Landmarks are extracted using classifier 6.


Stefan Ziegler
email: stefan.ziegler@irisa.fr

Bogdan Ludusan
email: bogdan.ludusan@irisa.fr

Guillaume Gravier
email: guillaume.gravier@irisa.fr

Using Broad Phonetic Classes to Guide Search in Automatic Speech Recognition

Keywords: Viterbi decoding, broad phonetic classes, landmarks

This work presents a novel framework to guide the Viterbi decoding process of a hidden Markov model based speech recognition system by means of broad phonetic classes. In a first step, decision trees are employed, along with frame and segment based attributes, in order to detect broad phonetic classes in the speech signal. Then, the detected phonetic classes are used to reinforce paths in the search process, either at every frame or at phonetically significant landmarks. Results obtained on French broadcast news data show a relative improvement in word error rate of about 2% with respect to the baseline.

Introduction

State of the art hidden Markov model (HMM) based automatic speech recognition (ASR) often suffers a significant drop in performance when being confronted with unknown variations in speech, like new speaking styles or accents. Since the articulatory aspects of human speech production are well understood nowadays and HMM-based ASR captures only few of this phonetic knowledge in its models, several studies have been aiming at exploiting phonetic knowledge to achieve more robust ASR. Some approaches to speech recognition, like Lexical Access from Features (LAFF) [START_REF] Stevens | Toward a model for lexical access based on acoustic landmarks and distinctive features[END_REF] and event-based system recognition [START_REF] Juneja | Speech recognition based on phonetic features and acoustic landmarks[END_REF], rely exclusively on phonetic knowledge by detecting perceptual important events (landmarks) in the speech signal, events which provide information about articulatory gestures and the basic sound contrasts in speech. Phonetic knowledge can also serve as additional source of knowledge inside probabilistic frameworks, like in [START_REF] Glass | A probabilistic framework for segment-based speech recognition[END_REF], where landmark and segmentbased information is combined inside a probabilistic ASR system. The integration of articulatory information into a decoding graph was also studied in [START_REF] Sainath | Island-driven search using broad phonetic classes[END_REF], where broad phonetic classes (BPCs) were used to concentrate computational efforts during decoding inside reliable regions of speech. In [START_REF] Hasegawa-Johnson | Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop[END_REF], support vector machines are used to detect phonetic landmarks for performing lattice rescoring in a two-pass ASR system.

In this paper, we extend the framework initially proposed in [START_REF] Gravier | Towards phonetically-driven hidden Markov models: can we incorporate phonetic landmarks in HMM-based ASR?[END_REF], which used landmarks in the form of BPCs to guide a Viterbi decoding process, in order to obtain improved word graphs. In that study, manually detected landmarks served as anchor points during decoding to strictly prune paths incompatible with the presented landmark information. Our first contri-This work was partially supported by the Agence Nationale de la Recherche in the framework of the ASH project. The authors would also like to thank Régine André-Obrecht and Jérôme Farinas for providing them with the implementation of the forward-backward divergence method.

bution lies in presenting a BPC classification system that organizes selected attributes into decision trees to detect BPCs in the speech signal. We compare two approaches to BPC classification. The first one estimates the BPC for every frame in the signal, while the second approach predicts the BPC only for certain landmark-frames. These landmarks are obtained by estimating the most salient points of articulatory gestures. Furthermore, we replace the existing phonetically driven decoding by a more flexible implementation that enhances paths according to broad phonetic knowledge.

The paper is organized as follows: In the first part, we present the phonetically driven Viterbi decoding and provide details about our BPC classification system. The evaluation section presents the experimental setup and reports recognition results, while the paper concludes with an outlook on future work.

ASR Driven by Phonetic Knowledge

In HMM-based ASR, the Viterbi algorithm uses dynamic programming to keep track of the best path reaching state (j, t) by computing a score S(j, t) = max i S(i, t -1) + log(a ij ) + log(p(yt|j)) + R(j, t). (1) S(i, t -1) is the score for being in state i at time t -1, log(aij) denotes the transition log-probability from state i to j and log(p(yt|j)) represents the likelihood of the feature vector yt conditional to state j. R (j, t) introduces knowledge about the correct path at frame t into the Viterbi decoding by reinforcing state j by R (j, t) = λj,t • Rmax. λj,t is a binary value, indicating whether state j is compatible with the external knowledge source and Rmax acts as the enhancement factor, limiting the influence of R (j, t) on the overall score. If there is no knowledge about the correct path at frame t, R (j, t) = 0 for all j. Broad phonetic knowledge can easily be introduced into the decoder, since every state j is directly linked to one BPC.

BPCs organize phonemes into different classes according to similarities in their articulatory gestures. The phonemes of one BPC share the same acoustic properties, while the classes are maximal discriminant towards each other. Our intention is to add a BPC classifier parallel to the existing HMM-based ASR system, that estimates the presence of BPCs in the signal. For each frame for which a BPC is detected, all states that are member of this BPC will be activated with λj,t = 1, while the remaining states are set to λj,t = 0. In the following section, we will provide details about our BPC classification system.

Detection of Broad Phonetic Information

Our BPC classifier estimates the probability pBP Cc (t) of c = 8 classes: vowels, nasals, approximants, plosives, fricatives (further divided by voiced and unvoiced) and a final group repre- For each segment, the frame for which the detected segment-BPC has the highest frame probability is chosen as the landmark.

senting the non-speech events inbreath and silence. Introducing these automatically detected BPCs into the decoding is not straightforward, since there is a trade-off between providing as much phonetic information as possible, while preventing the insertion of wrong information. We study two factors that influence this trade-off. The first factor is the place where pBP Cc (t) is estimated: either for every frame in the speech signal or only for selected landmark frames, where the acoustic correlates of articulatory gestures are assumed to be most salient. While the framebased approach has the possibility to provide the most amount of phonetic information, unreliable parts of speech, for example phoneme transitions, are likely to misguide decoding due to misclassified BPCs. In contrast to that, landmarks do not predict the BPC for every frame, but could still guide towards the correct path by correctly enhancing at least one frame inside each phoneme.

The second factor concerns the confusion between acoustically similar BPCs, like approximants and vowels. To reduce the phonetic errors introduced into the decoding, we set a minimum BPC probability p d and merge the two most likely BPCs into a more general phonetic class, until the probability of the most likely BPC exceeds p d . By increasing p d , the phonetic information at every classified frame might become very broad, while the errors introduced into the decoding will decrease.

In section 3.1, we will focus on the extraction of attributes for the frame and landmark-based BPC classifier. While both approaches use frame-based acoustic attributes for prediction, landmarks rely additionally on segmentation to estimate articulatory movements. The details about the mapping of those attributes onto their respective BPCs by using decision trees are provided in section 3.2.

Frame and Segment-based Attributes

At the frame level, the acoustic content of the BPCs is modeled by m acoustic parameters (APs) xt,m that are extracted for each frame t. Short-term contextual information is provided by adding a small context window of one frame around t, resulting in m • 3 attributes per frame.

To extract landmarks from the speech signal, we divide the speech signal into a sequence of segments, using the forwardbackward divergence method [START_REF] André-Obrecht | A new statistical approach for the automatic segmentation of continuous speech signals[END_REF], which sequentially detects segment boundaries by comparing the parameters of a long term with that of a short term auto-regressive (AR) model. A significant change in the AR-models is supposed to indicate a change in the articulatory movement. Segmentation groups the framebased representation of speech x = x1, . . . , xt, into a sequence of segments X = X1, . . . , Xτ with Xτ containing all frames xt inside the τ -th segment. The acoustic content of a segment is described by the 3 frames at the center of each segment, to capture the spectral transitions of the articulatory movement. Contextual information of subsequent segments is again provided by adding the attributes of the two neighbouring segments to a total of 9 • m attributes for each segment. After the classifier has estimated the BPC probabilities of a segment, one frame inside the segment is chosen as the landmark, by selecting the frame where the most probable segment-BPC has the highest frame probability.

Decision Trees for BPC Prediction

The design of our BPC classification system is motivated by landmark-based approaches to speech recognition, like LAFF [START_REF] Stevens | Toward a model for lexical access based on acoustic landmarks and distinctive features[END_REF], which provide a set of expert rules that map acoustic cues to their respective phonetic classes. By employing decision trees, we aim at automatically organizing the attributes into a similar set of binary rules. Decision trees provide reliable probability estimates, since the class probabilities are derived from the distribution of the classes at the end nodes of the tree and can accommodate for nominal information, in our case information about the channel bandwidth. We compensate potentially unstable behaviour of single trees by bootstrap aggregating.

While Mel-frequency cepstral coefficients (MFCCs) are state of the art for the acoustic modeling of phonemes, BPCs might equally be discriminated by broader, yet more robust APs, like low-level features describing general spectral shapes. We employ correlation-based feature selection [START_REF] Hall | Correlation-based feature selection for machine learning[END_REF] to obtain the most discriminative APs from a large amount of attributes, which include MFCCs (with first and second order derivatives), spectral shapes (e.g. spectral centroid), temporal statistics (e.g. zero-crossing rate), energy measurements (e.g. bark bands) and formant information.

The speaker diarization information of the speech recognizer provides the possibility to normalize the APs on a common range for each speaker. Our normalization method first divides the attributes of the m-th AP for each speaker into q quantiles. Normalization is performed by replacing every attribute xt,m with x norm t,m ∈ {1, 2, . . . , q} by x norm t,m = arg min q |xt,m -cq,m|. cq,m is the median of the values inside the q-th quantile of the m-th attribute for the current speaker. Constant effects of the channel and speaker on xt,m, often modeled as changes in mean and variance, will not affect the quantiles of the distribution. Besides increasing the robustness, this method reduces memory requirements, since x norm t,m requires only 7 bits of memory, assuming q = 128. Furthermore, in combination with decision trees, only 128 different split positions for each node have to be considered during training, which equally reduces computation time.

Experiments

The speech recognizer used in this paper is a two-pass system, with the first pass generating a word graph and the second pass rescoring the previously obtained graph using more complex models. The acoustic models employed in the first pass are word-internal triphones with 4,019 distinct states and 32 Gaussians per state and word trigrams are used as language model. The rescoring pass uses 4-grams as language model and crossword triphone models with 6,000 states and 32 Gaussians each.

The acoustic models are trained on 150 hours of mainly planned speech in studio broadcast environments. As proposed in [START_REF] Gravier | Towards phonetically-driven hidden Markov models: can we incorporate phonetic landmarks in HMM-based ASR?[END_REF], we employ phonetic constraints only in the first pass, to generate better word graphs. The word error rates (WERs) reported are calculated on the final hypotheses obtained after all passes. Decision trees are built using the WEKA toolbox [START_REF] Hall | The WEKA data mining software: an update[END_REF] with information gain as splitting criterion, reduced-error pruning and 30 bootstrap aggregating iterations on a 50% sample of the training data. The APs are extracted using YAAFE [START_REF] Mathieu | YAAFE, an easy to use and efficient audio feature extraction software[END_REF] and the Snack Sound toolkit [START_REF] Sjlander | The snack sound toolkit[END_REF] and we additionally add the nominal attribute bandwidth, obtained from the diarization system, to discriminate telephone from wide band speech. Trees are trained on 1.67 million instances from 26 hours training data, for both frame and segment-based training. For the frame-based classifier, each training instance corresponds to the 3 • m attributes from the three frames at the center of each segment, while each instance of the segment-based classifier additionally contains the 6 • m attributes of the neighbouring segments. A segment was only used for training, if the large majority (e.g. 70%) of the segment was inside a phonemic boundary.

Experiments are conducted on radio broadcast news in French language from the ESTER2 campaign [START_REF] Galliano | The ESTER 2 evaluation campaign for the rich transcription of French broadcasts[END_REF], which contain news shows with regular broadcast speech (RFI), but also difficult tasks like debates (Inter) Feature selection resulted in 62 attributes, containing only few MFCCs, mostly derivatives. For computational reasons, we first used the 35 best ranked APs with 10 bagging iterations and 30% sample size to optimize classification parameters like pruning strategy or splitting criterion, considering only the wide band speech during training and testing. Table 1 compares the classification performance of the best settings obtained, but with different normalization methods of the APs. The best performing quantile normalization method is also displayed using the full 62 APs.

In Figure 3, we compare the precision-recall curve of the frame-based BPCs to the landmark BPCs. For both cases, the frames having a probability estimate pBP Cc (t) higher than the current threshold were used for evaluating the performance on a phoneme level. A phoneme was considered as correctly detected, as long as all classified frames inside the phoneme matched the phoneme label. As soon there was one falsely predicted frame inside the phoneme boundaries, it was regarded as a misclassified phoneme. If no frame inside a phoneme was above the current threshold, the phoneme was considered as a missed phoneme. While the segmentation generally over-segmented phonemes (see [START_REF] André-Obrecht | A new statistical approach for the automatic segmentation of continuous speech signals[END_REF]), some parts of speech were under-segmented, especially strongly co-articulated phonemes, which led to a maximum recall for landmarks of 84%. It can be seen that using our proposed method for landmark detection increases the phoneme precision, compared to the frame-based BPCs at equal recall.

Table 2 illustrates the relation between phonetic information, as the the average cardinality of the detected BPCs introduced into the system, and phoneme errors for several values for p d . Increasing p d reduces classification errors at the cost of less phonetic information, since many BPCs were merged into very general phonetic classes. Figure 2 displays the improvement in WER as a function of Rmax for four different experimental settings. Each setting was applied on two radio stations of the development set. The left column corresponds to recognition driven by landmark BPCs, while the recognition with frame-based BPCs is on the right. p d was not employed in the upper two rows, while p d = 0.95 was used in the lower two rows. If no p d option is used, frame-based BPCs are very sensitive towards the choice of Rmax and improvement in WER is only obtained inside a small window. Furthermore, the two different radio shows differ in their optimal Rmax. As soon as less errors are introduced into the system, by using p d = 0.95 or introducing BPCs only at landmarks, the exact choice of Rmax gets less important, since Rmax rarely enhances the wrong path. For all values of p d being tested on the development set, both, frame and landmark-driven decoding improved the WER at the optimal Rmax.

The pairing Rmax and p d was optimized on the development set for both, the landmark and frame-based approach and the two optimal pairs were then applied on the test set. The overall improvement in WER with respect to the 23.5% WER of the baseline of the test set was 0.4% for the frame-based BPCs and 0.3% for the landmark-based BPCs. The statistical significance of the WER improvement was tested using a Wilcoxon signed rank test and it proved to be significant at the 5% level, for both, the landmark and frame-based case. Radio Inter achieved the least gain on the development set with 0.2% (frame) and 0.3% (landmarks) but confirmed this small gain on the test set. RFI was not included in the development set, but gained 0.3% and 0.4%. Africa 1 performed well and improved the WER 0.8% and 0.7% on the test set. While TVME gained 0.5% for both approaches on the development set, this was not confirmed on the test set, where two recordings containing partly non-native speakers increased the WER.

Conclusions

In this paper, we proposed a method for driving the Viterbi decoding of a HMM-based ASR system by automatically detected BPCs. We compared a frame-based and a landmark-based approach to BPC detection using decision trees and we limited phonetic errors by merging similar BPCs according to estimated BPC probabilities. While both approaches achieved a similar improvement on broadcast news shows, landmarks proved to be more robust towards the choice of the maximum enhancement factor.

With the promising results phonetically driven decoding was able to achieve in this study, one part of our future work is to improve the existing BPC classification. Furthermore, efforts will be made towards combining frame and landmarkbased BPC classification and exploring the integration of additional knowledge sources. Finally, we plan to explore methods that introduce knowledge about BPCs into the word graph of the rescoring step.

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Figure 3 :

 3 Figure 3: Precision-recall curve, evaluated on the phoneme level for the frame and landmark-based BPCs.

  Frame and segment-based BPC detection. Both approaches estimate BPC probabilities for each frame, respectively segment.

		BPC Classification		merge similar BPCs	knowledge
	xt frames	pBP C 1 (t) . . .	. . .		. . . λ1(t)	. . .
		pBP Cc (t)		while max pBP Cc (t) < p d	λj(t)
	Xτ segments	pBP C 1 (t) . . .	. . .	merge two most likely BPCs	λ1(t) . . .	. . .
		pBP Cc (t)			λj(t)
	Figure 1:

Table 1 :

 1 Comparison of classification performance on the development set. and Africa 1). Our development set contains 16 recordings from the ESTER2 development set, covering three different news shows (135 min Africa 1, 60 min Inter, 60 min TVME) and the test set contains 23 recordings (90 min Africa 1, 70 min RFI, 60 min Inter, 60 min TVME) from the ESTER2 test set.

		or accentuated speech (TVME
	normalization	m APs accuracy
	not normalized	35	69.7
	mean variance	35	71.7
	quantiles (q = 128)	35	71.8
	quantiles (q = 128)	62	72.3

Table 2 :

 2 Relation between average size of the BPCs detected and phoneme errors on the development set for different p d . The total number of speech and non-speech symbols is 40.

		AVG BPC size	incorrect phonemes [%]
	p d	landmark frame landmark	frame
	-	8	8	31	85
	0.8	14	16	13	45
	0.95	22	24	4		17
	4.2. Speech Recognition
		baseline	frame	landmark
	p d	-		0.8		0.6
	Rmax	-		2		8
	broadcast	dev	test	dev	test	dev	test
	Inter	21.9 18.7	21.7 18.4	21.6 18.6
	RFI	-	17.6	-	17.3	-	17.2
	Africa 1	45.1 31.5	44.6 30.7	44.7 30.8
	TVME	30.1 24.1	29.6 24.2	29.6 24.4

Table 3 :

 3 WER[%] for the optimal p d and Rmax on the development and test set.