Reference Eigen-Environment and Speaker Weighting for Robust Speech Recognition

• Main Authors: Hung-Hsiang Fang, 方泓翔
• Other Authors: 廖元甫
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/qgqywg

碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 96 === In this study a reference eigen-environment and speaker weighting (RESW) method is proposed for online HMM adaptation. RESW establishes multiple eigen-MLLR subspaces as the set of a priori knowledge according to certain affecting factors, such as noise type, SNR, male and female. It then projects an input test utterance simultaneously into the set of eigen-subspaces and optimally synthesizes out a set of suitable HMMs. The proposed RESW was evaluated on Aurora 2 multi-condition training task. Experimental results showed that average word error rate (WER) of 6.12% was achieved. Moreover, RESW not only outperformed the multi-condition training baseline (Multi-Con., 13.72%) but also the blind ETSI advanced DSR front-end (ETSI-Adv., 8.65%) and the histogram equalization (HEQ, 8.66%) and the non-blind reference model weighting (RMW, 7.29%) and Eigen-MLLR (6.14%) approaches.