Acoustic Training from Heterogeneous Data Sources: Experiments in Mandarin Conversational Telephone Speech Transcription
Stavros Tsakalidis, Bill Byrne · 2006
In this paper we investigate the use of heterogeneous data sources for acoustic training. We describe an acoustic normalization procedure for enlarging an ASR acoustic training set with out-of-domain acoustic data. A larger in-domain training set is created by effectively transforming the out-of-domain data before incorporation in training. Baseline experimental results in Mandarin conversational telephone speech transcription show that a simple attempt to add out-of-domain data degrades performance. Preliminary experiments assess the effectiveness of the proposed cross-corpus acoustic normalization. Furthermore, we investigate the behavior of speaker adaptive training in conjunction with the cross-corpus normalization procedure.