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. |
| Keywords: | |
| Projects: |
Idiap SNSF-MULTI IM2 |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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