Abstract
This paper describes the Speech Activity Detection (SAD) and Speaker Diarization (SPKR) systems that were developed by the Athens Information Technology in the scope of the NIST RT-06S evaluations. The SAD system performs classification of recorded frames into speech and non-speech, using Linear Discriminant Analysis (LDA), while the SPKR one initially segments recordings into speech intervals based on the Bayesian Information Criterion (BIC), and then applies a two-step clustering strategy to group segments from the same speaker together. Following a discussion of the intrinsics of the two systems, we report and comment on our results on the RT-06S corpus [20].
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Rentzeperis, E., Stergiou, A., Boukis, C., Pnevmatikakis, A., Polymenakos, L.C. (2006). The 2006 Athens Information Technology Speech Activity Detection and Speaker Diarization Systems. 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_34
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DOI: https://doi.org/10.1007/11965152_34
Publisher Name: Springer, Berlin, Heidelberg
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