Mixed-Lingual Acoustic Modeling of Hidden Conditional Random Field for Robust Speech Recognition

• Main Authors: Shih-Han Huang, 黃詩涵
• Other Authors: 洪維廷
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/66507400162186510812

碩士 === 元智大學 === 通訊工程學系 === 99 === This thesis presents the robust training techniques for hidden conditional random fiels (HCRF)-based acoustic modeling of Mandarin/English mixed-lingual speech recognition. Two issues were dealt with: (1) mixed-lingual speech recognition against with noise effects and (2) cross-lingual errors in mixed-lingual speech recognition. We solved first issue with the REST algorithm and reduce the errors in second issue with a discriminative training algorithm combined by the REST algorithm(D-REST). The experimental results indicate that 16.4% averaged error rate reduction by the HCRF-based framework is achieved under ROVER_2 noise environment compared with the result by the traditional HMM approach. In additional, the cross-lingual error is improved significantly with the HCRF-framework in mixed-lingual speech recognition.