Systematic comparison of BIC-based speaker segmentation systems

Unsupervised speaker change detection is addressed in this paper. Three speaker segmentation systems are examined. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic fusion scheme, and applies the Bayesian Information Criterion (BIC). The second system consists of three modules. In the first module, a second-order statistic-measure is extracted; the Euclidean distance and the T2 Hotelling statistic are applied sequentially in the second module; and BIC is utilized in the third module. The third system, first uses a metric-based approach, in order to detect potential speaker change points, and then the BIC criterion is applied to validate the previously detected change points. Experiments are carried out on a dataset, which is created by concatenating speakers from the TIMIT database. A systematic performance comparison among the three systems is carried out by means of one-way ANOVA method and post hoc Tukey’s method

Computationally Efficient and Robust BIC-Based Speaker Segmentation

An algorithm for automatic speaker segmentation based on the Bayesian information criterion (BIC) is presented. BIC tests are not performed for every window shift, as previously, but when a speaker change is most probable to occur. This is done by estimating the next probable change point thanks to a model of utterance durations. It is found that the inverse Gaussian fits best the distribution of utterance durations. As a result, less BIC tests are needed, making the proposed system less computationally demanding in time and memory, and considerably more efficient with respect to missed speaker change points. A feature selection algorithm based on branch and bound search strategy is applied in order to identify the most efficient features for speaker segmentation. Furthermore, a new theoretical formulation of BIC is derived by applying centering and simultaneous diagonalization. This formulation is considerably more computationally efficient than the standard BIC, when the covariance matrices are estimated by other estimators than the usual maximum-likelihood ones. Two commonly used pairs of figures of merit are employed and their relationship is established. Computational efficiency is achieved through the speaker utterance modeling, whereas robustness is achieved by feature selection and application of BIC tests at appropriately selected time instants. Experimental results indicate that the proposed modifications yield a superior performance compared to existing approaches

Unsupervised video indexing on audiovisual characterization of persons

Cette thèse consiste à proposer une méthode de caractérisation non-supervisée des intervenants dans les documents audiovisuels, en exploitant des données liées à leur apparence physique et à leur voix. De manière générale, les méthodes d'identification automatique, que ce soit en vidéo ou en audio, nécessitent une quantité importante de connaissances a priori sur le contenu. Dans ce travail, le but est d'étudier les deux modes de façon corrélée et d'exploiter leur propriété respective de manière collaborative et robuste, afin de produire un résultat fiable aussi indépendant que possible de toute connaissance a priori. Plus particulièrement, nous avons étudié les caractéristiques du flux audio et nous avons proposé plusieurs méthodes pour la segmentation et le regroupement en locuteurs que nous avons évaluées dans le cadre d'une campagne d'évaluation. Ensuite, nous avons mené une étude approfondie sur les descripteurs visuels (visage, costume) qui nous ont servis à proposer de nouvelles approches pour la détection, le suivi et le regroupement des personnes. Enfin, le travail s'est focalisé sur la fusion des données audio et vidéo en proposant une approche basée sur le calcul d'une matrice de cooccurrence qui nous a permis d'établir une association entre l'index audio et l'index vidéo et d'effectuer leur correction. Nous pouvons ainsi produire un modèle audiovisuel dynamique des intervenants.This thesis consists to propose a method for an unsupervised characterization of persons within audiovisual documents, by exploring the data related for their physical appearance and their voice. From a general manner, the automatic recognition methods, either in video or audio, need a huge amount of a priori knowledge about their content. In this work, the goal is to study the two modes in a correlated way and to explore their properties in a collaborative and robust way, in order to produce a reliable result as independent as possible from any a priori knowledge. More particularly, we have studied the characteristics of the audio stream and we have proposed many methods for speaker segmentation and clustering and that we have evaluated in a french competition. Then, we have carried a deep study on visual descriptors (face, clothing) that helped us to propose novel approches for detecting, tracking, and clustering of people within the document. Finally, the work was focused on the audiovisual fusion by proposing a method based on computing the cooccurrence matrix that allowed us to establish an association between audio and video indexes, and to correct them. That will enable us to produce a dynamic audiovisual model for each speaker

Robust ASR using Support Vector Machines

The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units. In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad

Intonational Phrases for Speech Summarization

Extractive speech summarization approaches select relevant segments of spoken documents and concatenate them to generate a summary. The extraction unit chosen, whether a sentence, syntactic constituent, or other segment, has a significant impact on the overall quality and fluency of the summary. Even though sentences tend to be the choice of most the extractive speech summarizers, in this paper, we present the results of an empirical study indicating that intonational phrases are better units of extraction for summarization. Our study compared four types of input segmentation: sentences, two pause-based segmentation, and intonational phrases (IP). We found that IPs are the best candidates for extractive summarization, improving over the second highest-performing approach, sentence-based summarization, by 8.2% F-measure

A Study of Accomodation of Prosodic and Temporal Features in Spoken Dialogues in View of Speech Technology Applications

Inter-speaker accommodation is a well-known property of human speech and human interaction in general. Broadly it refers to the behavioural patterns of two (or more) interactants and the effect of the (verbal and non-verbal) behaviour of each to that of the other(s). Implementation of thisbehavior in spoken dialogue systems is desirable as an improvement on the naturalness of humanmachine interaction. However, traditional qualitative descriptions of accommodation phenomena do not provide sufficient information for such an implementation. Therefore, a quantitativedescription of inter-speaker accommodation is required. This thesis proposes a methodology of monitoring accommodation during a human or humancomputer dialogue, which utilizes a moving average filter over sequential frames for each speaker. These frames are time-aligned across the speakers, hence the name Time Aligned Moving Average (TAMA). Analysis of spontaneous human dialogue recordings by means of the TAMA methodology reveals ubiquitous accommodation of prosodic features (pitch, intensity and speech rate) across interlocutors, and allows for statistical (time series) modeling of the behaviour, in a way which is meaningful for implementation in spoken dialogue system (SDS) environments.In addition, a novel dialogue representation is proposed that provides an additional point of view to that of TAMA in monitoring accommodation of temporal features (inter-speaker pause length and overlap frequency). This representation is a percentage turn distribution of individual speakercontributions in a dialogue frame which circumvents strict attribution of speaker-turns, by considering both interlocutors as synchronously active. Both TAMA and turn distribution metrics indicate that correlation of average pause length and overlap frequency between speakers can be attributed to accommodation (a debated issue), and point to possible improvements in SDS “turntaking” behaviour. Although the findings of the prosodic and temporal analyses can directly inform SDS implementations, further work is required in order to describe inter-speaker accommodation sufficiently, as well as to develop an adequate testing platform for evaluating the magnitude ofperceived improvement in human-machine interaction. Therefore, this thesis constitutes a first step towards a convincingly useful implementation of accommodation in spoken dialogue systems

The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems

Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for their conversational and multilingual support. We find that word error rates for natural conversational data in 6 languages remain abysmal, and that overlap remains a key challenge (study 1). This impacts especially the recognition of conversational words (study 2), and in turn has dire consequences for downstream intent recognition (study 3). Our findings help to evaluate the current state of conversational ASR, contribute towards multidimensional error analysis and evaluation, and identify phenomena that need most attention on the way to build robust interactive speech technologies

Adaptive speaker diarization of broadcast news based on factor analysis

The introduction of factor analysis techniques in a speaker diarization system enhances its performance by facilitating the use of speaker specific information, by improving the suppression of nuisance factors such as phonetic content, and by facilitating various forms of adaptation. This paper describes a state-of-the-art iVector-based diarization system which employs factor analysis and adaptation on all levels. The diarization modules relevant for this work are: the speaker segmentation which searches for speaker boundaries and the speaker clustering which aims at grouping speech segments of the same speaker. The speaker segmentation relies on speaker factors which are extracted on a frame-by-frame basis using eigenvoices. We incorporate soft voice activity detection in this extraction process as the speaker change detection should be based on speaker information only and we want it to disregard the non-speech frames by applying speech posteriors. Potential speaker boundaries are inserted at positions where rapid changes in speaker factors are witnessed. By employing Mahalanobis distances, the effect of the phonetic content can be further reduced, which results in more accurate speaker boundaries. This iVector-based segmentation significantly outperforms more common segmentation methods based on the Bayesian Information Criterion (BIC) or speech activity marks. The speaker clustering employs two-step Agglomerative Hierarchical Clustering (AHC): after initial BIC clustering, the second cluster stage is realized by either an iVector Probabilistic Linear Discriminant Analysis (PLDA) system or Cosine Distance Scoring (CDS) of extracted speaker factors. The segmentation system is made adaptive on a file-by-file basis by iterating the diarization process using eigenvoice matrices adapted (unsupervised) on the output of the previous iteration. Assuming that for most use cases material similar to the recording in question is readily available, unsupervised domain adaptation of the speaker clustering is possible as well. We obtain this by expanding the eigenvoice matrix used during speaker factor extraction for the CDS clustering stage with a small set of new eigenvoices that, in combination with the initial generic eigenvoices, models the recurring speakers and acoustic conditions more accurately. Experiments on the COST278 multilingual broadcast news database show the generation of significantly more accurate speaker boundaries by using adaptive speaker segmentation which also results in more accurate clustering. The obtained speaker error rate (SER) can be further reduced by another 13% relative to 7.4% via domain adaptation of the CDS clustering. (C) 2017 Elsevier Ltd. All rights reserved