Abstract
In this paper, we describe the IBM system submitted to the NIST Rich Transcription Spring 2006 (RT06s) evaluation campaign for automatic speech activity detection (SAD). This SAD system has been developed and evaluated on CHIL lecture meeting data using far-field microphone sensors, namely a single distant microphone (SDM) configuration and a multiple distant microphone (MDM) condition. The IBM SAD system employs a three-class statistical classifier, trained on features that augment traditional signal energy ones with features that are based on acoustic phonetic likelihoods. The latter are obtained using a large speaker-independent acoustic model trained on meeting data. In the detection stage, after feature extraction and classification, the resulting sequence of classified states is further collapsed into segments belonging to only two classes, speech or silence, following two levels of smoothing. In the MDM condition, the process is repeated for every available microphone channel, and the outputs are combined based on a simple majority voting rule, biased towards speech. The system performed well at the RT06s evaluation campaign, resulting to 8.62% and 5.01% “speaker diarization error” in the SDM and MDM conditions respectively.
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Marcheret, E., Potamianos, G., Visweswariah, K., Huang, J. (2006). The IBM RT06s Evaluation System for Speech Activity Detection in CHIL Seminars. In: Renals, S., Bengio, S., Fiscus, J.G. (eds) Machine Learning for Multimodal Interaction. MLMI 2006. Lecture Notes in Computer Science, vol 4299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11965152_29
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DOI: https://doi.org/10.1007/11965152_29
Publisher Name: Springer, Berlin, Heidelberg
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