An Information Theoretic Approach to Speaker Diarization of Meeting Data

A speaker diarization system based on an information theoretic framework is described. The problem is formulated according to the {\em Information Bottleneck} (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. This solves the problem of choosing the distance between speech segments, which becomes the Jensen-Shannon divergence as it arises from the IB objective function optimization. We discuss issues related to speaker diarization using this information theoretic framework such as the criteria for inferring the number of speakers, the trade-off between quality and compression achieved by the diarization system, and the algorithms for optimizing the objective function. Furthermore we benchmark the proposed system against a state-of-the-art system on the NIST RT06 (Rich Transcription) data set for speaker diarization of meeting. The IB based system achieves a Diarization Error Rate of $23.2\%$ as compared to $23.6\%$ of the baseline system. This approach being mainly based on non-parametric clustering, it runs significantly faster then the baseline HMM/GMM based system, resulting in faster-then-real-time diarization

An Information Theoretic Approach to Speaker Diarization of Meeting Recordings

In this thesis we investigate a non parametric approach to speaker diarization for meeting recordings based on an information theoretic framework. The problem is formulated using the Information Bottleneck (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, the IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. The distance between speech segments is selected as the Jensen-Shannon divergence as it arises from the IB objective function optimization. In the first part of the thesis, we explore IB based diarization with Mel frequency cepstral coefficients (MFCC) as input features. We study issues related to IB based speaker diarization such as optimizing the IB objective function, criteria for inferring the number of speakers. Furthermore, we benchmark the proposed system against a state-of-the-art systemon the NIST RT06 (Rich Transcription) meeting data for speaker diarization. The IB based system achieves similar speaker error rates (16.8%) as compared to a baseline HMM/GMM system (17.0%). This approach being non parametric clustering, perform diarization six times faster than realtime while the baseline is slower than realtime. The second part of thesis proposes a novel feature combination system in the context of IB diarization. Both speaker clustering and speaker realignment steps are discussed. In contrary to current systems, the proposed method avoids the feature combination by averaging log-likelihood scores. Two different sets of features were considered – (a) combination of MFCC features with time delay of arrival features (b) a four feature stream combination that combines MFCC, TDOA, modulation spectrum and frequency domain linear prediction. Experiments show that the proposed system achieve 5% absolute improvement over the baseline in case of two feature combination, and 7% in case of four feature combination. The increase in algorithm complexity of the IB system is minimal with more features. The system with four feature input performs in real time that is ten times faster than the GMM based system

New insights into hierarchical clustering and linguistic normalization for speaker diarization

Face au volume croissant de données audio et multimédia, les technologies liées à l'indexation de données et à l'analyse de contenu ont suscité beaucoup d'intérêt dans la communauté scientifique. Parmi celles-ci, la segmentation et le regroupement en locuteurs, répondant ainsi à la question 'Qui parle quand ?' a émergé comme une technique de pointe dans la communauté de traitement de la parole. D'importants progrès ont été réalisés dans le domaine ces dernières années principalement menés par les évaluations internationales du NIST. Tout au long de ces évaluations, deux approches se sont démarquées : l'une est bottom-up et l'autre top-down. L'ensemble des systèmes les plus performants ces dernières années furent essentiellement des systèmes types bottom-up, cependant nous expliquons dans cette thèse que l'approche top-down comporte elle aussi certains avantages. En effet, dans un premier temps, nous montrons qu'après avoir introduit une nouvelle composante de purification des clusters dans l'approche top-down, nous obtenons des performances comparables à celles de l'approche bottom-up. De plus, en étudiant en détails les deux types d'approches nous montrons que celles-ci se comportent différemment face à la discrimination des locuteurs et la robustesse face à la composante lexicale. Ces différences sont alors exploitées au travers d'un nouveau système combinant les deux approches. Enfin, nous présentons une nouvelle technologie capable de limiter l'influence de la composante lexicale, source potentielle d'artefacts dans le regroupement et la segmentation en locuteurs. Notre nouvelle approche se nomme Phone Adaptive Training par analogie au Speaker Adaptive TrainingThe ever-expanding volume of available audio and multimedia data has elevated technologies related to content indexing and structuring to the forefront of research. Speaker diarization, commonly referred to as the who spoke when?' task, is one such example and has emerged as a prominent, core enabling technology in the wider speech processing research community. Speaker diarization involves the detection of speaker turns within an audio document (segmentation) and the grouping together of all same-speaker segments (clustering). Much progress has been made in the field over recent years partly spearheaded by the NIST Rich Transcription evaluations focus on meeting domain, in the proceedings of which are found two general approaches: top-down and bottom-up. Even though the best performing systems over recent years have all been bottom-up approaches we show in this thesis that the top-down approach is not without significant merit. Indeed we first introduce a new purification component leading to competitive performance to the bottom-up approach. Moreover, while investigating the two diarization approaches more thoroughly we show that they behave differently in discriminating between individual speakers and in normalizing unwanted acoustic variation, i.e.\ that which does not pertain to different speakers. This difference of behaviours leads to a new top-down/bottom-up system combination outperforming the respective baseline system. Finally, we introduce a new technology able to limit the influence of linguistic effects, responsible for biasing the convergence of the diarization system. Our novel approach is referred to as Phone Adaptive Training (PAT).PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF