An overview of automatic speaker diarization systems

Tranter, S.E.; Reynolds, Douglas A.

doi:10.1109/tasl.2006.878256

Cited by 511 publications

(329 citation statements)

References 28 publications

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“…[2,3], although top-down systems also achieve competitive results [4]. While some have reported that bottom-up approaches are more robust than their top-down counterparts [5] our own work [6] shows that the two approaches give comparable results, with neither being consistently superior to the other.…”

Section: Introductionmentioning

confidence: 86%

Linguistic influences on bottom-up and top-down clustering for speaker diarization

Bozonnet

Wang

Evans

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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While bottom-up approaches have emerged as the standard, default approach to clustering for speaker diarization we have always found the top-down approach gives equivalent or superior performance. Our recent work shows that significant gains in performance can be obtained when cluster purification is applied to the output of topdown systems but that it can degrade performance when applied to the output of bottom-up systems. This paper demonstrates that these observations can be accounted for by factors unrelated to the speaker and that they can impact more strongly on the performance of bottom-up clustering strategies than top-down strategies. Experimental results confirm that clusters produced through top-down clustering are better normalized against phone variation than those produced through bottom-up clustering and that this accounts for the observed inconsistencies in purification performance. The work highlights the need for marginalization strategies which should encourage convergence toward different speakers rather than toward nuisance factors such as that those related to the linguistic content.

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Section: Introductionmentioning

confidence: 86%

Linguistic influences on bottom-up and top-down clustering for speaker diarization

Bozonnet

Wang

Evans

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…This paper simply defines TDOA as the TDOA between the first channel X 1 (ω, f ) and others 1 . Finally, TDOA of the m-th channel in the f -th frame τ m (f ) is computed as follows:…”

Section: A Estimation Of Number Of Sound Sources 1) Tdoa Estimationmentioning

confidence: 99%

“…Speaker diarization research mainly tackles the simultaneous estimation of speaker segmentation (voice activity detection) and clustering (number of speaker estimation). Beside monaural signal based methods [1], [2], microphone array technologies tackles this by introducing spatial information about the speakers. However, most of the existing methods assume that the microphone location is given to estimate the direction of arrival of speakers [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Nakamura

Mizumoto

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents a method for estimating the number, as well as the activity periods of spatially distributed sound sources using an uncalibrated microphone array. This methodology is applied for the purposes of speaker diarization. In general, speaker diarization has difficulty with: 1) estimating the number of sound sources (speakers), and 2) activity detection of multiple sound sources including overlap of utterances. Several microphone array based techniques have already tackled these challenges. However, existing methods mainly assume that the steering vectors for the microphone array are calibrated in advance to identify sound sources, which is difficult to satisfy when ad-hoc or flexible microphone arrays are used. Thus our approach estimates the number of sound sources blindly in two steps. First, Time Delay of Arrival (TDOA) of the observed signal is clustered. Second, the sound source activity is detected by clustering the long-term spatial spectrum using the TDOA based steering vector for each cluster. The validity of the algorithm is confirmed by both synthesized signals and a real-world flexible microphone array application.

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“…The class types considered may vary by application but a typical partitioning might distinguish pure music, pure speech, noise, combined speech and music and combined speech and noise (Tranter & Reynolds, 2006). The resultant partitioning may provide useful metadata for the purpose of flexible access, but such partitioning is also an important prerequisite for speech-to-text transcription systems (e.g.…”

Section: Automatic Annotationmentioning

confidence: 99%

Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey

Marsden

Mackenzie

Lindsay

et al. 2007

Literary and Linguistic Computing

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This paper examines the actual and potential use of software tools in research in the arts and humanities focussing on audiovisual materials such as recorded speech, music, video and film. The quantity of such materials available to researchers is massive and rapidly expanding. Researchers need to locate the material of interest in the vast quantity available, and to organise and process the material once collected. Locating and organising often depend on metadata and tags to describe the actual content, but standards for metadata for audiovisual materials are not widely adopted. Content-based

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An overview of automatic speaker diarization systems

Cited by 511 publications

References 28 publications

Linguistic influences on bottom-up and top-down clustering for speaker diarization

Linguistic influences on bottom-up and top-down clustering for speaker diarization

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey

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