Title :
A new HMM/LVQ hybrid algorithm for speech recognition
Author :
Katagiri, Shigeru ; Lee, Chin-Hui
Author_Institution :
AT&T Bell Labs., Murray Hill, NJ, USA
Abstract :
It is shown that by combining the discriminative power of learning vector quantization (LVQ) training algorithms and the capability of modeling temporal variations of a hidden Markov model (HMM) into a hybrid algorithm, the performance of an HMM-based recognition algorithm is significantly improved. The hybrid algorithm was tested in a multispeaker, isolated word mode, using a highly confusable vocabulary consisting of the nine English E-set words. The average word accuracy for the original HMM-based system was 62%. When the LVQ classifier was incorporated, the word accuracy increased to 81%
Keywords :
Markov processes; speech recognition; E-set words; HMM-based recognition algorithm; HMM/LVQ hybrid algorithm; LVQ classifier; LVQ training algorithm; discriminative power; hidden Markov model; highly confusable vocabulary; isolated word mode; learning vector quantization; speech recognition; temporal variations; Acoustic distortion; Artificial neural networks; Hidden Markov models; Laboratories; Pattern recognition; Speech recognition; Testing; Vector quantization; Visual perception; Vocabulary;
Conference_Titel :
Global Telecommunications Conference, 1990, and Exhibition. 'Communications: Connecting the Future', GLOBECOM '90., IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
0-87942-632-2
DOI :
10.1109/GLOCOM.1990.116659
