Multi-rate HMM quantization for speech recognition

被引：0

作者：

Vasilache, Marcel ^{[1
]}

机构：

[1] Nokia, FIN-33721 Tampere, Finland

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12 | 2008年

关键词：

hidden Markov models; quantization; speech recognition;

D O I：

10.1109/ICASSP.2008.4518616

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper refines the idea of scalar quantization for hidden Markov model (HMM) parameters which was introduced in an earlier contribution. With the proposed multi-rate approach it is shown that an increased model compression can be achieved with a significant computational complexity reduction while also closely preserving the recognition performance of the original models.

引用

页码：4341 / 4344

页数：4

共 9 条

[1] Subspace distribution clustering hidden Markov model
Bocchieri, E
Mak, BKW
[J]. IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, 2001, 9 (03): : 264 - 275
[2] KISS I, 2002, EUROSPEECH SCANDINAV, P1265
[3] LEPPANEN J, 2005, INTERSPEECH EUROSPEE, P2965
[4] Direct training of subspace distribution clustering hidden Markov model
Mak, BKW
Bocchieri, E
[J]. IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, 2001, 9 (04): : 378 - 387
[5] Tan ZH, 2008, ADV PATTERN RECOGNIT, P1, DOI 10.1007/978-1-84800-143-5
[6] VASILACHE M, 2004, ICASSP 2004, P113
[7] VASILACHE M, 2000, ICSSLP 2000, P871
[8] VASILACHE M, 2001, EUROSPEECH SCANDINAV, P1265
[9] Viikki O, 1998, INT CONF ACOUST SPEE, P733, DOI 10.1109/ICASSP.1998.675369

← 1 →