Unsupervised speaker change detection using probabilistic pattern matching

Malegaonkar, A.; Ariyaeeinia, Aladdin M.; Sivakumaran, P.; Fortuna, J.

doi:10.1109/lsp.2006.873656

Cited by 17 publications

(15 citation statements)

References 7 publications

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“…Another BIC-variant metric, referred to as cross-BIC and introduced in [67], [68], involves the computation of cross-likelihood: the likelihood of a first segment according to a model tuned from the second segment and vice versa. In [69], different techniques for likelihood normalization are presented and are referred to as bilateral scoring.…”

Section: Segmentationmentioning

confidence: 99%

Speaker Diarization: A Review of Recent Research

Anguera

Bozonnet²,

Evans³

et al. 2012

IEEE Trans. Audio Speech Lang. Process.

571

349

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Abstract-Speaker diarization is the task of determining "who spoke when?" in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher-level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.

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Section: Segmentationmentioning

confidence: 99%

Speaker Diarization: A Review of Recent Research

Anguera

Bozonnet²,

Evans³

et al. 2012

IEEE Trans. Audio Speech Lang. Process.

571

349

View full text Add to dashboard Cite

show abstract

“…However, bilateral scoring involves combining the above forward score with a reverse score obtained by matching utterance x against the richer model obtained through the use of utterance y. It should be further emphasised that fusing the reverse score with the traditional GMM-UBM forward score can be specifically beneficial for the real-world applications, which are likely to involve reference and test speech data of varied lengths [6][7][8] .…”

Section: Weighted Bilateral Scoring (Wbs)mentioning

confidence: 99%

Open-set speaker identification with diverse-duration speech data

2015

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Rawande Karadaghi, Heinz Hertlein, and, Aladdin Ariyaeeinia, "Open-set speaker identification with diverse-duration speech data", in Proceedings of SPIE 9457, Biometric and Surveillance Technology for Human Activity Identification XII, Baltimore, USA, 20 April 2015. DOI:10.1117/12.2176335?? 2015 SPIE.The concern in this paper is an important category of applications of open-set speaker identification in criminal investigation, which involves operating with short and varied duration speech. The study presents investigations into the adverse effects of such an operating condition on the accuracy of open-set speaker identification, based on both GMMUBM and i-vector approaches. The experiments are conducted using a protocol developed for the identification task, based on the NIST speaker recognition evaluation corpus of 2008. In order to closely cover the real-world operating conditions in the considered application area, the study includes experiments with various combinations of training and testing data duration. The paper details the characteristics of the experimental investigations conducted and provides a thorough analysis of the results obtained

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“…These include the introduction of the XBIC measure which is derived from comparing BIC with a distance measure for hidden Markov models (HMMs) [9]. A previous study in the field by the authors has resulted in the development of an effective method which is termed bilateral scoring-based speaker change detection (BLS-SCD) [10]. This involves the use of a probabilistic pattern matching approach that has been shown to be more effective than both BIC and XBIC [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Speaker Change Detection Using Adapted Gaussian Mixture Models

Malegaonkar

Ariyaeeinia

Sivakumaran

2007

IEEE Trans. Audio Speech Lang. Process.

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A new approach to speaker change detection is proposed and investigated. The method, which is based on a probabilistic framework, provides an effective means for tackling the problem posed by phonetic variation in high-resolution speaker change detection. Additionally, the approach incorporates the capability for dealing with undesired effects of variations in speech characteristics. Using the experimental investigations conduced with clean and broadcast news audio, it is shown that the proposed method is significantly more effective than the currently popular techniques for speaker change detection. To enhance the computational efficiency of the proposed method, modified implementation algorithms are introduced which are based on the exploitation of the redundant operations and a fast scoring procedure. It is shown that, through the use of the proposed fast algorithm, the computational efficiency of the approach can be increased by over 77% without significant reduction in its accuracy. The paper discusses the principles and characteristics of the proposed speaker change detection method, and provides a detailed description of its efficient implementation. The experiments, investigating the performance of the proposed method and its effectiveness in relation to other approaches, are described and an analysis of the results is presented.

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Unsupervised speaker change detection using probabilistic pattern matching

Cited by 17 publications

References 7 publications

Speaker Diarization: A Review of Recent Research

Speaker Diarization: A Review of Recent Research

Open-set speaker identification with diverse-duration speech data

Efficient Speaker Change Detection Using Adapted Gaussian Mixture Models

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