Title :
Two discriminative training schemes of GMM for language identification
Author :
Dan, Qu ; Bingxi, Wang ; Qiang, Zhang
Author_Institution :
Dept. of Information Sci., Information Eng. Univ., Zhengzhou, China
fDate :
31 Aug.-4 Sept. 2004
Abstract :
In this paper, two discriminative training procedures for a Gaussian mixture model (GMM) language identification system are described. One is based on maximum mutual information criterion (MMI), the other uses minimum classification error (MCE) criterion. Both the proposals are based on the generalized probabilistic descent (GPD) algorithm formulated to estimate the GMM parameters. The evaluation is conducted using the OGI multi-language telephone speech corpus. The experimental results show such system is very effective in language identification tasks.
Keywords :
Gaussian processes; natural languages; speech recognition; Gaussian mixture model; discriminative training scheme; generalized probabilistic descent algorithm; language identification system; maximum mutual information criterion; minimum classification error criterion; multilanguage telephone speech corpus; Chromium; Error analysis; Influenza; Information science; Maximum likelihood estimation; Natural languages; Robustness; Speech analysis; Speech recognition; Wire;
Conference_Titel :
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN :
0-7803-8406-7
DOI :
10.1109/ICOSP.2004.1452742
