DocumentCode :
3161865
Title :
Towards single pass discriminative training for speech recognition
Author :
Hsiao, Roger ; Schultz, Tanja
Author_Institution :
Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2012
fDate :
25-30 March 2012
Firstpage :
4093
Lastpage :
4096
Abstract :
This paper describes how we can combine our previously proposed fast extended Baum-Welch algorithm and generalized discriminative feature transformation to achieve single pass discriminative training, which we only process the data once. Compared to the state of the art training procedure, which uses feature space maximum mutual information (fMMI) and boosted maximum mutual information (BMMI), our proposed training procedure can achieve around 80% of the improvement available from discriminative training. We also show that if we are allowed to process the data twice, it is possible to achieve almost all of the improvement. We evaluate different training procedures on various large scale tasks using Iraqi and modern standard Arabic speech recognition systems.
Keywords :
learning (artificial intelligence); natural languages; speech recognition; Arabic speech recognition system; BMMI; Iraqi speech recognition system; boosted maximum mutual information; fMMI; fast extended Baum-Welch algorithm; feature space maximum mutual information; generalized discriminative feature transformation; single pass discriminative training; Acoustics; Equations; Mathematical model; Speech recognition; Training; Transforms; Vectors; Speech recognition; discriminative training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
ISSN :
1520-6149
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2012.6288818
Filename :
6288818
Link To Document :
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