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 [BibTeX] [Marc21]
Using KL-divergence and multilingual information to improve ASR for under-resourced languages
Type of publication: Conference paper
Citation: Imseng_ICASSP_2012
Booktitle: Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2012
Month: March
Pages: 4869--4872
Location: Kyoto
Abstract: Setting out from the point of view that automatic speech recognition (ASR) ought to benefit from data in languages other than the target language, we propose a novel Kullback-Leibler (KL) divergence based method that is able to exploit multilingual information in the form of universal phoneme posterior probabilities conditioned on the acoustics. We formulate a means to train a recognizer on several different languages, and subsequently recognize speech in a target language for which only a small amount of data is available. Taking the Greek SpeechDat(II) data as an example, we show that the proposed formulation is sound, and show that it is able to outperform a current state-of-the-art HMM/GMM system. We also use a hybrid Tandem-like system to further understand the source of the benefit.
Projects Idiap
Authors Imseng, David
Bourlard, Hervé
Garner, Philip N.
Added by: [UNK]
Total mark: 0
  • Imseng_ICASSP_2012.pdf