A Study on Minimum Phone Error Discriminative Training for Mandarin Chinese Speaker Adaptation

• Main Authors: Niu Hsueh-Wen, 牛學文
• Other Authors: Jyh-Shing Jang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/21269686851264592834

碩士 === 國立清華大學 === 資訊系統與應用研究所 === 95 === In order to decrease the error rate of speech recognition, speaker adaptation techniques are often used to adjust speaker-dependent acoustic models. MLLR (Maximum Likelihood Linear Regression) and MAP (Maximum a Posteriori) are two of the most popular techniques in recent years. MLLR uses the technique of regression trees. It calculates the transform matrix for each leaf node of the tree. This makes it possible to use fewer sentences to decrease the error rate of HMM-based speech recognition. However, while we examined the recognition result, we found that although the overall error rate decreased, but the error rate of certain confusable phones was higher. In order to solve this problem, we propose the use MPE (Minimum Phone Error Discriminative Training) to solve this problem. We use the same corpus as the one in MLLR adaptation, and use MPE to make further adjustment to acoustic models which have been adapted by MLLR. Besides, we tested several methods such as adjusting I-smoothing factors or phone lattices to obtain finer result. Besides, we also introduced a new approach to reduce the computation time of both the lattice construction and the MPE- weight calculation, all based on a better use of n-best recognition (3.3.3). Furthermore, we proposed a new method to combine the statistic result of regression trees and I-smoothing factor based on the observation result of chapter 2.1.3. Experiment results show that it can further reduce the error rate.