Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

Robust audio indexing for Dutch spoken-word collections

Abstract—Whereas the growth of storage capacity is in accordance with widely acknowledged predictions, the possibilities to index and access the archives created is lagging behind. This is especially the case in the oral history domain and much of the rich content in these collections runs the risk to remain inaccessible for lack of robust search technologies. This paper addresses the history and development of robust audio indexing technology for searching Dutch spoken-word collections and compares Dutch audio indexing in the well-studied broadcast news domain with an oral-history case-study. It is concluded that despite significant advances in Dutch audio indexing technology and demonstrated applicability in several domains, further research is indispensable for successful automatic disclosure of spoken-word collections

Factor analysis for speaker segmentation and improved speaker diarization

Speaker diarization includes two steps: speaker segmentation and speaker clustering. Speaker segmentation searches for speaker boundaries, whereas speaker clustering aims at grouping speech segments of the same speaker. In this work, the segmentation is improved by replacing the Bayesian Information Criterion (BIC) with a new iVector-based approach. Unlike BIC-based methods which trigger on any acoustic dissimilarities, the proposed method suppresses phonetic variations and accentuates speaker differences. More specifically our method generates boundaries based on the distance between two speaker factor vectors that are extracted on a frame-by frame basis. The extraction relies on an eigenvoice matrix so that large differences between speaker factor vectors indicate a different speaker. A Mahalanobis-based distance measure, in which the covariance matrix compensates for the remaining and detrimental phonetic variability, is shown to generate accurate boundaries. The detected segments are clustered by a state-of-the-art iVector Probabilistic Linear Discriminant Analysis system. Experiments on the COST278 multilingual broadcast news database show relative reductions of 50% in boundary detection errors. The speaker error rate is reduced by 8% relative

‘Did the speaker change?’: Temporal tracking for overlapping speaker segmentation in multi-speaker scenarios

Diarization systems are an essential part of many speech processing applications, such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker domains. This thesis will focus on the first task of the diarization process, that being the task of speaker segmentation which can be thought of as trying to answer the question ‘Did the speaker change?’ in an audio recording. This thesis starts by showing that time-varying pitch properties can be used advantageously within the segmentation step of a multi-talker diarization system. It is then highlighted that an individual’s pitch is smoothly varying and, therefore, can be predicted by means of a Kalman filter. Subsequently, it is shown that if the pitch is not predictable, then this is most likely due to a change in the speaker. Finally, a novel system is proposed that uses this approach of pitch prediction for speaker change detection. This thesis then goes on to demonstrate how voiced harmonics can be useful in detecting when more than one speaker is talking, such as during overlapping speaker activity. A novel system is proposed to track multiple harmonics simultaneously, allowing for the determination of onsets and end-points of a speaker’s utterance in the presence of an additional active speaker. This thesis then extends this work to explore the use of a new multimodal approach for overlapping speaker segmentation that tracks both the fundamental frequency (F0) and direction of arrival (DoA) of each speaker simultaneously. The proposed multiple hypothesis tracking system, which simultaneously tracks both features, shows an improvement in segmentation performance when compared to tracking these features separately. Lastly, this thesis focuses on the DoA estimation part of the newly proposed multimodal approach. It does this by exploring a polynomial extension to the multiple signal classification (MUSIC) algorithm, spatio-spectral polynomial (SSP)-MUSIC, and evaluating its performance when using speech sound sources.Open Acces

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