Low-latency online speaker tracking on the AMI Corpus of meeting conversations

Zamalloa, Maider; Rodríguez-Fuentes, Luis Javier; Bordel, Germán; Peñagarikano, Mikel; Uribe, Juan Pedro

doi:10.1109/icassp.2010.5495089

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…This duration seems to be rational because speech utterances shorter that one second are rare. The same duration settings are applied in [11] with good achievements.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Speaker Tracking Using Eigendecomposition and an Index Tree of Reference Models

Moattar¹

2011

ETRI J

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This paper focuses on online speaker tracking for telephone conversations and broadcast news. Since the online applicability imposes some limitations on the tracking strategy, such as data insufficiency, a reliable approach should be applied to compensate for this shortage. In this framework, a set of reference speaker models are used as side information to facilitate online tracking. To improve the indexing accuracy, adaptation approaches in eigenvoice decomposition space are proposed in this paper. We believe that the eigenvoice adaptation techniques would help to embed the speaker space in the models and hence enrich the generality of the selected speaker models. Also, an index structure of the reference models is proposed to speed up the search in the model space. The proposed framework is evaluated on 2002 Rich Transcription Broadcast News and Conversational Telephone Speech corpus as well as a synthetic dataset. The indexing errors of the proposed framework on telephone conversations, broadcast news, and synthetic dataset are 8.77%, 9.36%, and 12.4%, respectively. Using the index tree structure approach, the run time of the proposed framework is improved by 22%.

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“…This duration seems to be rational because speech utterances shorter that one second are rare. The same duration settings are applied in [11] with good achievements.…”

Section: Methodsmentioning

confidence: 99%

“…This has reduced the threshold dependency on the number of speakers. In [11], a speaker tracking system is presented which outputs decisions at one second intervals by scoring the segments on generic speaker models. The length of segments is empirically set to provide good time resolution and small latency for tracking.…”

Section: Introductionmentioning

confidence: 99%

Speaker Tracking Using Eigendecomposition and an Index Tree of Reference Models

Moattar¹

2011

ETRI J

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“…number of speakers or speaker models). A number of online diarization [11,12,13,14,15] and speaker tracking [16,17] solutions have been reported. These use online speaker clustering algorithms [18,19].…”

Section: Prior Workmentioning

confidence: 99%

Low-latency speaker spotting with online diarization and detection

Patino¹,

Yin²,

Delgado³

et al. 2018

The Speaker and Language Recognition Workshop (Odyssey 2018)

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This paper introduces a new task termed low-latency speaker spotting (LLSS). Related to security and intelligence applications, the task involves the detection, as soon as possible, of known speakers within multi-speaker audio streams. The paper describes differences to the established fields of speaker diarization and automatic speaker verification and proposes a new protocol and metrics to support exploration of LLSS. These can be used together with an existing, publicly available database to assess the performance of LLSS solutions also proposed in the paper. They combine online diarization and speaker detection systems. Diarization systems include a naive, over-segmentation approach and fully-fledged online diarization using segmental i-vectors. Speaker detection is performed using Gaussian mixture models, i-vectors or neural speaker embeddings. Metrics reflect different approaches to characterise latency in addition to detection performance. The relative performance of each solution is dependent on latency. When higher latency is admissible, i-vector solutions perform well; embeddings excel when latency must be kept to a minimum. With a need to improve the reliability of online diarization and detection, the proposed LLSS framework provides a vehicle to fuel future research in both areas. In this respect, we embrace a reproducible research policy; results can be readily reproduced using publicly available resources and open source codes.

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“…In Reynolds and Torres-Carrasquillo [110], MFCC features for each cluster is processed with CMN to increase robustness against channel distortion in their offline speaker diarization systems. While in Zamalloa et al [158], the dynamic CMN approach is applied in their online speaker tracking system.…”

Section: Cepstral Mean Normalizationmentioning

confidence: 99%