Segmentation into audio document speakers: A new approach based on the one- class support vector methods
Keywords:Acoustic signal processing, Audio recordings, Data mining, Database systems, Information retrieval, Support vector machines, Digital sound files, Speaker diarization, Text files
AbstractWith recent and continued increases in the number of available sound archives (radio, TV, Web,...), effective methods must be established to facilitate the process of searching for information within massive databases. Of less complexity than the original sound file but nevertheless containing a summary of important information pertaining to the signal, text files (index files) are linked to the digital sound files. An example of relevant information found in the text file is as follows: 45 minutes of speech, 1 minute of music, 10 speakers (6 men and 4 women). These index files, stored with the original signal, will contribute considerably to the information retrieval process, allowing an immediate and direct access to the information sought. If one would like to know who speaks and when in a sound file, the index key is hence the speaker. A preliminary stage of a speaker indexing system is speaker diarization. State-of-the-art speaker diarization techniques require two main steps: speaker turn detection which consists of detecting speaker turn times, that is boundaries of audio file segments where only one speaker is present, followed by a clustering step which consists of labelling the previous segments in terms of speakers. These two stages require a metric to be defined in order to compare and groups speech segments. This paper presents a novel approach for the speaker diarization of audio recordings. The proposed approach uses a metric based on one-class Support Vector Machines (SVM-I), introduced recently by one of the authors, for the speaker change detection and clustering tasks. Through many experiments using two databases of broadcast recordings, we demonstrate the relevance and superiority of this approach compared to the traditional method based on the generalized likelihood ratio using bayesian information criterion (RVG-BIC).
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