Complete-linkage clustering for voice activity detection in audio and visual speech
Ghaemmaghami, Houman, Dean, David, Kalantari, Shahram, Sridharan, Sridha, & Fookes, Clinton (2015) Complete-linkage clustering for voice activity detection in audio and visual speech. In Interspeech 2015: 16th Annual Conference of the International Speech Communication Association, 6-10 September 2015, Maritim International Congress Center, Dresden, Germany.
We propose a novel technique for conducting robust voice activity detection (VAD) in high-noise recordings. We use Gaussian mixture modeling (GMM) to train two generic models; speech and non-speech. We then score smaller segments of a given (unseen) recording against each of these GMMs to obtain two respective likelihood scores for each segment. These scores are used to compute a dissimilarity measure between pairs of segments and to carry out complete-linkage clustering of the segments into speech and non-speech clusters. We compare the accuracy of our method against state-of-the-art and standardised VAD techniques to demonstrate an absolute improvement of 15% in half-total error rate (HTER) over the best performing baseline system and across the QUT-NOISE-TIMIT database. We then apply our approach to the Audio-Visual Database of American English (AVDBAE) to demonstrate the performance of our algorithm in using visual, audio-visual or a proposed fusion of these features.
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|Item Type:||Conference Paper|
|Keywords:||Voice activity detection, High noise, Gaussian mixture modeling, complete-linkage clustering|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 [please consult the authors]|
|Deposited On:||07 Jul 2015 23:36|
|Last Modified:||26 Sep 2015 12:20|
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