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.

View at publisher


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.

Impact and interest:

2 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

49 since deposited on 07 Jul 2015
9 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 85160
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page