An improved maximum model distance approach for HMM-based speech recognition systems
✍ Scribed by Q.H He; S Kwong; K.F Man; K.S Tang
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 143 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
✦ Synopsis
This paper proposes an improved maximum model distance (IMMD) approach for HMM-based speech recognition systems based on our previous work [S. Kwong, Q.H. He, K.F. Man, K.S. Tang. A maximum model distance approach for HMM-based speech recognition, Pattern Recognition 31 (3) (1998) 219}229]. It de"nes a more realistic model distance de"nition for HMM training, and utilizes the limited training data in a more e!ective manner. Discriminative information contained in the training data was used to improve the performance of the recognizer. HMM parameter adjustment rules were induced in details. Theoretical and practical issues concerning this approach are also discussed and investigated in this paper. Both isolated word and continuous speech recognition experiments showed that a signi"cant error reduction could be achieved by IMMD when compared with the maximum model distance (MMD) criterion and other training methods using the minimum classi"cation error (MCE) and the maximum mutual information (MMI) approaches.