DocumentCode
2865594
Title
The 1994 HTK large vocabulary speech recognition system
Author
Woodland, P.C. ; Leggetter, C.J. ; Odell, J.J. ; Valtchev, V. ; Young, S.J.
Author_Institution
Dept. of Eng., Cambridge Univ., UK
Volume
1
fYear
1995
fDate
9-12 May 1995
Firstpage
73
Abstract
This paper describes recent work on the HTK large vocabulary speech recognition system. The system uses tied-state cross-word context-dependent mixture Gaussian HMMs and a dynamic network decoder that can operate in a single pass. In the last year the decoder has been extended to produce word lattices to allow flexible and efficient system development, as well as multi-pass operation for use with computationally expensive acoustic and/or language models. The system vocabulary can now be up to 65 k words, the final acoustic models have been extended to be sensitive to more acoustic context (quinphones), a 4-gram language model has been used and unsupervised incremental speaker adaptation incorporated. The resulting system gave the lowest error rates on both the H1-P0 and H1-C1 hub tasks in the November 1994 ARPA CSR evaluation
Keywords
Gaussian processes; acoustic signal processing; decoding; grammars; hidden Markov models; natural languages; speech recognition; 4-gram language model; ARPA CSR evaluation; HTK large vocabulary speech recognition system; acoustic context; acoustic models; context-dependent mixture Gaussian HMM; dynamic network decoder; error rates; language models; multi-pass operation; quinphones; system development; system vocabulary; tied-state cross-word HMM; unsupervised incremental speaker adaptation; word lattices; Context modeling; Decision trees; Decoding; Hidden Markov models; Lattices; Loudspeakers; Natural languages; Speech recognition; Training data; Vocabulary;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location
Detroit, MI
ISSN
1520-6149
Print_ISBN
0-7803-2431-5
Type
conf
DOI
10.1109/ICASSP.1995.479276
Filename
479276
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