improved Speech recognition using Discriminative integration of multiple local Classifiers in lattice rescoring

摘要：

Model combination is a popular technique to integrate several knowledge sources into automatic speech recognition for better system accuracy. In this paper, we report our recent work on the integration of the hidden Markov model based acoustic model, the multi-layer perceptron based phoneme classifier and Gaussian mixture model based tone classifier in lattice rescoring. Moreover, we use discriminative model weight training to tune the impact of the heterogeneous models according to different phonetic contexts for better model interpolation. Experimental results on continuous mandarin speech recognition show a 8.2％ improvement can be obtained using the combination of the three models. We have also evaluated four context dependent weighting schemes using discriminative trained scaling factors. It is also shown by introducing left final dependent contexts, a 4.1％ further recognition error reduction can be further obtained.

关键词： Discriminative model combination speech recognition Multi-layer perceptron minimum phone error

作者: Hao Huang Bing Hu Li

作者单位: Xinjiang laboratory of multi-language information technology Department of Information Science and Engineering, Xinjiang University, Urumqi, P. R. China

会议类型: 国际会议

会议名称: 2011 International Conference on Electronics and Optoelectronics(2011电子学与光电子学国际会议 ICEOE 2011)

会议地点: 大连

会议语种:英文

页码: 1102-1105

在线出版日期: 2011-07-29（万方平台首次上网日期，不代表论文的发表时间）