A Mandarin Speech Recognition System Using Weighted Finite-State Transducer

• Main Authors: Lin, Ang-Hsing, 林昂星
• Other Authors: Wang, Yih-Ru
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/28065850188969895626

碩士 === 國立交通大學 === 電信工程研究所 === 100 === This study focuses on how to use a Weighted Finite-State Transducer (WFST) to construct a Mandarin Speech Recognition System (MSRS). It first introduces algorithms for WFST, as well as different levels of speech model to represent the Finite-State Machine (FSM) graph, and integrates into the MSRS. The experimental results indentify the Word Error Rate (WER) at about 55% is related to the appearance of OOV words, and statistics shows that one OOV word results in 2.4 words error averagely in MSRS. According to the statistical results, it shows that the names OOV words accounts for about 30%, and in which three words Chinese names accounts for about 23%, In order to reduce the negative impact of the OOV words results in the MSRS, we introduce a hierarchical language model, training name model to assist lower WER. The test corpus uses for the read-type long sentences TCC300 corpus. The 13.76% WER is obtained by using HTK two-stage recognition, while use of WFST RT=13 can achieve the same WER, the recognition speed is about 15 times faster than the traditional HTK. Besides, we construct OOVs names model in the language model layer and placed in the MSRS, this effectively reduces the WER at about 0.12%.