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|a 10.1109/TPAMI.2017.2648793
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|a eng
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| 100 |
1 |
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|a Gebru, Israel D
|e verfasserin
|4 aut
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| 245 |
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|a Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion
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|c 2018
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 19.03.2019
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| 500 |
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|a Date Revised 19.03.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of several participants engaged in multi-party interaction while they move around and turn their heads towards the other participants rather than facing the cameras and the microphones. Multiple-person visual tracking is combined with multiple speech-source localization in order to tackle the speech-to-person association problem. The latter is solved within a novel audio-visual fusion method on the following grounds: binaural spectral features are first extracted from a microphone pair, then a supervised audio-visual alignment technique maps these features onto an image, and finally a semi-supervised clustering method assigns binaural spectral features to visible persons. The main advantage of this method over previous work is that it processes in a principled way speech signals uttered simultaneously by multiple persons. The diarization itself is cast into a latent-variable temporal graphical model that infers speaker identities and speech turns, based on the output of an audio-visual association process, executed at each time slice, and on the dynamics of the diarization variable itself. The proposed formulation yields an efficient exact inference procedure. A novel dataset, that contains audio-visual training data as well as a number of scenarios involving several participants engaged in formal and informal dialogue, is introduced. The proposed method is thoroughly tested and benchmarked with respect to several state-of-the art diarization algorithms
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Ba, Sileye
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Xiaofei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Horaud, Radu
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 5 vom: 19. Mai, Seite 1086-1099
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:40
|g year:2018
|g number:5
|g day:19
|g month:05
|g pages:1086-1099
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|u http://dx.doi.org/10.1109/TPAMI.2017.2648793
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