Speaker diarization of spontaneous meeting room conversations

Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization systems have isolated three main issues with the systems; overlapping speech, effects of background noise and speech/nonspeech detection errors on clustering, and signficant performance variance between different systems. In this thesis we focuss on addressing these issues in diarization. We propose new features based on structure of a conversation such as silence and speaker change statistics for overlap detection. The features are estimated from a long-term context (3-4 seconds) and are used to estimate the probability of overlap at a given instant. These probabilities are later incorporated into acoustic feature based overlap detector as prior probabilities. Experiments on several meeting corpora reveal that overlap detection is improved significantly by the proposed method and this consequently reduces the diarization error. To address the issues arising from background noise, errors in speech/non-speech detection and capture speaker discriminative information in the signal, we propose two methods. In the first method, we propose Information Bottleneck with Side Information (IBSI) based diarization to supress artefacts of background noise and non-speech segments introduced into clustering. In the second method, we show that the phoneme transcript of a given recording carries useful information for speaker diarization. This obervation was used in estimation of phoneme background model which is used for diarization in Information Bottleneck (IB) framework. Both the methods achieve significant reduction in error on various meeting corpora. We train different artificial neural network (ANN) architectures to extract speaker discriminant features and use these features as input to speaker diarization systems. The ANNs are trained to perform related tasks such as speaker comparison, speaker classification and auto encoding. The bottleneck layer activations from these networks are used as features for speaker diarization. Experiments on different meeting corpora revealed that combination of MFCCs and ANN features reduces the diarization error. To address the issue of performance variations across different sytems, we propose feature level combination of HMM/GMM and IB diarization systems. The combination does not require any changes to the original systems. The output of IB system is used to generate features which when combined with MFCCs in a HMM/GMM system reduce diarization error.

Sree Harsha Yella

Speaker diarization is the task of identifying “who spoke when” in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization systems have isolated three main issues with the systems; overlapping speech, effects of background noise and speech/non- speech detection errors on clustering, and signficant performance variance between different systems. In this thesis we focuss on addressing these issues in diarization. We propose new features based on structure of a conversation such as silence and speaker change statistics for overlap detection. The features are estimated from a long-term context (3-4 seconds) and are used to estimate the probability of overlap at a given instant. These probabilities are later incorporated into acoustic feature based overlap detector as prior prob- abilities. Experiments on several meeting corpora reveal that overlap detection is improved significantly by the proposed method and this consequently reduces the diarization error. To address the issues arising from background noise, errors in speech/non-speech detection and capture speaker discriminative information in the signal, we propose two methods. In the first method, we propose Information Bottleneck with Side Information (IBSI) based diarization to supress artefacts of background noise and non-speech segments introduced into clustering. In the second method, we show that the phoneme transcript of a given recording carries useful information for speaker diarization. This obervation was used in estimation of phoneme background model which is used for diarization in Information Bottleneck (IB) framework. Both the methods achieve significant reduction in error on various meeting corpora. We train different artificial neural network (ANN) architectures to extract speaker discriminant features and use these features as input to speaker diarization systems. The ANNs are trained to perform related tasks such as speaker comparison, speaker classification and auto encod- ing. The bottleneck layer activations from these networks are used as features for speaker diarization. Experiments on different meeting corpora revealed that combination of MFCCs and ANN features reduces the diarization error. To address the issue of performance variations across different sytems, we propose feature level combination of HMM/GMM and IB diarization systems. The combination does not require any changes to the original systems. The output of IB system is used to generate features which when combined with MFCCs in a HMM/GMM system reduce diarization error.

EPFL2015

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

Plamen Prodanov

Mobile service robots are going to play an increasing role in the society of humans. Voice-enabled interaction with service robots becomes very important, if such robots are to be deployed in real-world environments and accepted by the vast majority of potential human users. The research presented in this thesis addresses the problem of speech recognition integration in an interactive voice-enabled interface of a service robot, in particular a tour-guide robot. The task of a tour-guide robot is to engage visitors to mass exhibitions (users) in dialogue providing the services it is designed for (e.g. exhibit presentations) within a limited time. In managing tour-guide dialogues, extracting the user goal (intention) for requesting a particular service at each dialogue state is the key issue. In mass exhibition conditions speech recognition errors are inevitable because of noisy speech and uncooperative users of robots with no prior experience in robotics. They can jeopardize the user goal identification. Wrongly identified user goals can lead to communication failures. Therefore, to reduce the risk of such failures, methods for detecting and compensating for communication failures in human-robot dialogue are needed. During the short-term interaction with visitors, the interpretation of the user goal at each dialogue state can be improved by combining speech recognition in the speech modality with information from other available robot modalities. The methods presented in this thesis exploit probabilistic models for fusing information from speech and auxiliary modalities of the robot for user goal identification and communication failure detection. To compensate for the detected communication failures we investigate multimodal methods for recovery from communication failures. To model the process of modality fusion, taking into account the uncertainties in the information extracted from each input modality during human-robot interaction, we use the probabilistic framework of Bayesian networks. Bayesian networks are graphical models that represent a joint probability function over a set of random variables. They are used to model the dependencies among variables associated with the user goals, modality related events (e.g. the event of user presence that is inferred from the laser scanner modality of the robot), and observed modality features providing evidence in favor of these modality events. Bayesian networks are used to calculate posterior probabilities over the possible user goals at each dialogue state. These probabilities serve as a base in deciding if the user goal is valid, i.e. if it can be mapped into a tour-guide service (e.g. exhibit presentation) or is undefined – signaling a possible communication failure. The Bayesian network can be also used to elicit probabilities over the modality events revealing information about the possible cause for a communication failure. Introducing new user goal aspects (e.g. new modality events and related features) that provide auxiliary information for detecting communication failures makes the design process cumbersome, calling for a systematic approach in the Bayesian network modelling. Generally, introducing new variables for user goal identification in the Bayesian networks can lead to complex and computationally expensive models. In order to make the design process more systematic and modular, we adapt principles from the theory of grounding in human communication. When people communicate, they resolve understanding problems in a collaborative joint effort of providing evidence of common shared knowledge (grounding). We use Bayesian network topologies, tailored to limited computational resources, to model a state-based grounding model fusing information from three different input modalities (laser, video and speech) to infer possible grounding states. These grounding states are associated with modality events showing if the user is present in range for communication, if the user is attending to the interaction, whether the speech modality is reliable, and if the user goal is valid. The state-based grounding model is used to compute probabilities that intermediary grounding states have been reached. This serves as a base for detecting if the the user has reached the final grounding state, or wether a repair dialogue sequence is needed. In the case of a repair dialogue sequence, the tour-guide robot can exploit the multiple available modalities along with speech. For example, if the user has failed to reach the grounding state related to her/his presence in range for communication, the robot can use its move modality to search and attract the attention of the visitors. In the case when speech recognition is detected to be unreliable, the robot can offer the alternative use of the buttons modality in the repair sequence. Given the probability of each grounding state, and the dialogue sequence that can be executed in the next dialogue state, a tour-guide robot has different preferences on the possible dialogue continuation. If the possible dialogue sequences at each dialogue state are defined as actions, the introduced principle of maximum expected utility (MEU) provides an explicit way of action selection, based on the action utility, given the evidence about the user goal at each dialogue state. Decision networks, constructed as graphical models based on Bayesian networks are proposed to perform MEU-based decisions, incorporating the utility of the actions to be chosen at each dialogue state by the tour-guide robot. These action utilities are defined taking into account the tour-guide task requirements. The proposed graphical models for user goal identification and dialogue error handling in human-robot dialogue are evaluated in experiments with multimodal data. These data were collected during the operation of the tour-guide robot RoboX at the Autonomous System Lab of EPFL and at the Swiss National Exhibition in 2002 (Expo.02). The evaluation experiments use component and system level metrics for technical (objective) and user-based (subjective) evaluation. On the component level, the technical evaluation is done by calculating accuracies, as objective measures of the performance of the grounding model, and the resulting performance of the user goal identification in dialogue. The benefit of the proposed error handling framework is demonstrated comparing the accuracy of a baseline interactive system, employing only speech recognition for user goal identification, and a system equipped with multimodal grounding models for error handling.

EPFL2006

Exploitation de connaissances sémantiques externes dans les représentations vectorielles en recherche documentaire

Florian Seydoux

The work presented in this thesis deals with several problems met in information retrieval (IR), task which one can summarise as identifying, in a collection of "documents", a subset of documents carrying a sought information, i.e.. relevant for a request expressed by a user. In the case of textual documents, to which we limited ourselves within the framework of this thesis, a significant part of the difficulty lies in ambiguity inherent to human languages. The interaction with the user is also approached in our work, by studying a tool enabling a natural language access to a database. Finally, some techniques which permit the visualisation of large collections of documents are also presented. In this document we first of all describe the principal models of IR by highlighting the relations which exist with some manual technics of IR and document retrieval, developed during the past centuries. We present the principle of document indexing, allowing us to represent documents in a multidimensional space, and the use of this representation by a vectorial model. After having reviewed the principal improvements made these last years with vectorial research systems, including the preprocessings of collections, the indexing mechanism and measurements of similarities between documents, we detail some recent usecases of additional semantic resources (semantic dictionaries, thesaurus, networks, ontologies) reported in scientific literature for the indexing task. We then present more in detail the semantic indexing principle of textual documents by using a thesaurus, consisting in integrating in the document's representation space at least part of the informational contents of hierarchical semantic resources. We propose a general framework allowing us to describe and position various possible techniques to carry out the semantic indexing by adapting, if possible, the specificity of the descriptions resulting from the semantic resources to the data to be represented. We use this framework to describe three families of criteria usable for semantic indexing, each one having its own characteristics. For each of these families, we give the specific algorithms allowing the computation of the criteria. The first two families allow us to consider several criteria already known in feature selection. Moreover we show that, unfortunately, many of these criteria are in fact not very effective for the considered task. The third family allows us to introduce a completely new criterion, the Minimum Redundancy Cut criterion (MRC), built on the basis of the information theory and allowing us to obtain index terms having a probability of occurrence in the collection of documents as well balanced as possible. Finally, we treat the case of semantic index independent of the data (statically choosen), allowing a parameterisation of the level of generality of the index terms. Some of the criteria suggested for semantic indexing has been empirically evaluated. To judge their relevance, we used a well known vectorial system (the Smart IR system) and measured the performances of IR obtained with various reference collections. Those collections was indexed on the basis of the studied criterion, by taking into account the strongly structuring semantic relation of hyper/hyponymy ("is-a" relation), given by two different semantic resources. By comparing results obtained with the performances of a traditional indexing (using the lemmas of the words as representation space), we can show on one hand the relevance of the semantic indexings (in RD) and on the other hand the quality of the proposed criterion (MRC). Concerning man-machine interaction, we present a general outline allowing to build in a relatively fast and systematic way systems with mixed initiative, giving the human user a large (and natural) latitude in the control of the dialogue. This outline is usable in typical database research-task applications (where the database is hidden to the user, but the latter knows exactly which information they wish to find) as well as advice-task applications, for which the users does not necessarily have a precise idea of their needs, and uses the system not only for specifing their wishes, but also a set of propositions as a final result. We particularly stress the techniques allowing us to obtain a robust system, able to deal with speech recognizer failures. Concerning the visualisation of large textual data collections, we present an application of the correspondences analysis (allowing to highlight similarities and oppositions for various groups of entity, built on the basis of additional features present in the DB) to the case of patents data. In addition, we propose a method (based on the bootstrap replication principle) allowing us to determine a confidence interval for relative positionings of various groups, thus permit to immediately judge the reliability of the visually apparent similarities or oppositions.

EPFL2006

Speaker diarization of spontaneous meeting room conversations

Sree Harsha Yella

EPFL2015

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

Plamen Prodanov

EPFL2006

Exploitation de connaissances sémantiques externes dans les représentations vectorielles en recherche documentaire

Florian Seydoux

EPFL2006