DocumentCode :
2174983
Title :
Lattice-based unsupervised acoustic model training
Author :
Fraga-Silva, Thiago ; Gauvain, Jean-Luc ; Lamel, Lori
Author_Institution :
Spoken Language Process. Group, LIMSI-CNRS, Orsay, France
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
4656
Lastpage :
4659
Abstract :
Unsupervised acoustic model training has been successfully used to improve the performance of automatic speech recognition systems when only a small amount of manually transcribed data is available for the target domain. The most common approach is use automatic transcriptions to guide acoustic model estimation. However, since the best recognition hypotheses are known to contain errors, we propose to consider multiple transcription hypotheses during training. The idea is that the EM process can benefit from the estimated posterior probabilities of the hypotheses to converge to a better solution. The proposed unsupervised training method is based on lattices. Lattice-based training gives a relative improvement of 2.2% over 1-best training on a Broadcast News transcription task and converges faster with the iterative incremental training.
Keywords :
speech recognition; EM process; automatic speech recognition system; broadcast news transcription task; iterative incremental training; lattice-based unsupervised acoustic model training; target domain; Acoustics; Data models; Hidden Markov models; Lattices; Speech recognition; Training; Training data; Acoustic Modeling; Lattice-based training; Speech recognition; Unsupervised training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5947393
Filename :
5947393
Link To Document :
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