Audio Segmentation and Speaker Localization in Meeting Videos

Vajaria, H.; Islam, T.; Sarkar, Sudeep; Sankar, Ravi; Kasturi, Rangachar

doi:10.1109/icpr.2006.283

Cited by 28 publications

(24 citation statements)

References 7 publications

(12 reference statements)

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“…head gestures, which are also visible manifestations of speech [116] are used. While there has been relatively little work on using global body movements for inferring speaking status, some studies have been carried out [82], [117]- [119] that show promising initial results.…”

Section: Audiovisual Diarizationmentioning

confidence: 99%

Speaker Diarization: A Review of Recent Research

Anguera

Bozonnet²,

Evans³

et al. 2012

IEEE Trans. Audio Speech Lang. Process.

570

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: Audiovisual Diarizationmentioning

confidence: 99%

Speaker Diarization: A Review of Recent Research

Anguera

Bozonnet²,

Evans³

et al. 2012

IEEE Trans. Audio Speech Lang. Process.

570

349

View full text Add to dashboard Cite

show abstract

“…1). AV analysis is an emerging topic [8,26,30,38,40], prompting studies in a range of interesting tasks [2,17,24,25]. 1 Unimodal denoising and source separation are difficult when the intensity of the noise is very high (overwhelming the signal) and non stationary (structured).…”

Section: Introductionmentioning

confidence: 99%

Example-based cross-modal denoising

Segev

Schechner

Elad

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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“…Multimodal speaker diarization techniques (detection of who speaks when) based on the joint modeling of speech, facial and bodily cues (e.g., mouth movement, fidgeting, body pose, etc.) have been proposed in [1,6,8,[15][16][17][18]. To the best of our knowledge, the only work where diarization has been tried with solely visual cues is in [7], where the experiments showed that the performance decrease when the audio is absent.…”

Section: Introductionmentioning

confidence: 99%

Look at Who’s Talking: Voice Activity Detection by Automated Gesture Analysis

Cristani

Pesarin

Vinciarelli

et al. 2012

Communications in Computer and Information Science

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Abstract. This paper proposes an approach for Voice Activity Detection (VAD) based on the automatic measurement of gesturing. The main motivation of the work is that gestures have been shown to be tightly correlated with speech, hence they can be considered a reliable evidence that a person is talking. The use of gestures rather than speech for performing VAD can be helpful in many situation (e.g., surveillance and monitoring in public spaces) where speech cannot be obtained for technical, legal or ethical issues. The results show that the gesturing measurement approach proposed in this work achieves, on a frame-by-frame basis, an accuracy of 71 percent in distinguishing between speech and non-speech.

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Audio Segmentation and Speaker Localization in Meeting Videos

Cited by 28 publications

References 7 publications

Speaker Diarization: A Review of Recent Research

Speaker Diarization: A Review of Recent Research

Example-based cross-modal denoising

Look at Who’s Talking: Voice Activity Detection by Automated Gesture Analysis

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