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ICASSP
2008
IEEE

Speaker diarization of French broadcast news

11 years 6 months ago
Speaker diarization of French broadcast news
We report results on speaker diarization of French broadcast news and talk shows on current affairs. This speaker diarization process is a multistage segmentation and clustering system. One of the stages is agglomerative clustering using state-of-the-art speaker identiļ¬cation methods (SID). For the GMMs used in this stage, we tried many different feature parameters, including MFCCs, Gaussianized MFCCs, Gaussianized MFCCs with cepstral mean subtraction, and Gaussianized MFCCs with cepstral mean substraction containing only frames with high energy. We found that this last set of feature parameters gave the best results. Compared to Gaussianized MFCCs, these features reduced the diarization error rate (DER) by 12% on a development set and by 19% on a test set. We also combined clusters resulting from Gaussianized and non-Gaussianized feature sets. This cluster combination resulted in another 4% reduction in DER for both the development and the test sets. The best DER we have achieved i...
Vishwa Gupta, Gilles Boulianne, Patrick Kenny, Pie
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Vishwa Gupta, Gilles Boulianne, Patrick Kenny, Pierre Ouellet, Pierre Dumouchel
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