Improved Speech Activity Detection Using Cross-Channel Features for Recognition of Multiparty Meetings

TitleImproved Speech Activity Detection Using Cross-Channel Features for Recognition of Multiparty Meetings
Publication TypeConference Paper
Year of Publication2006
AuthorsBoakye, K., & Stolcke A.
Published inProceedings of the 9th International Conference on Spoken Language Processing (ICSLP-Interspeech 2006)
Page(s)1962-1965
Other Numbers2058
Abstract

We describe the development of a speech activity detection system using an HMM-based segmenter for automatic speech recognition on individual headset microphones in multispeaker meetings. We look at cross-channel features (energy and correlation based) to incorporate into the segmenter for the purpose of addressing errors related to cross-channel phenomena such as crosstalk. Results demonstrate that these features provide a marked improvement (18% relative) over a baseline system using single-channel features as well as an improvement (8% relative) over our previous solution of separate speech activity detection and cross-channel analysis. In addition, the simple cross-channel energy features are shown to be more robust - and consequently better performing - than the more common correlation-based features.

URLhttp://www.icsi.berkeley.edu/pubs/speech/boakye_stolcke.pdf
Bibliographic Notes

Proceedings of the 9th International Conference on Spoken Language Processing (ICSLP-Interspeech 2006), Pittsburgh, Pennsylvania, pp. 1962-1965

Abbreviated Authors

K. Boakye and A. Stolcke

ICSI Research Group

Speech

ICSI Publication Type

Article in conference proceedings