Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

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

Kofi Boakye (1), Andreas Stolcke (1,2)

(1) International Computer Science Institute, USA; (2) SRI International, USA

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.

Full Paper

Bibliographic reference.  Boakye, Kofi / Stolcke, Andreas (2006): "Improved speech activity detection using cross-channel features for recognition of multiparty meetings", In INTERSPEECH-2006, paper 1824-Wed3A1O.3.