Triple Hybrid Model And Cepstral Statistics Normalization Techniques For Robust Speech Recognition

• Main Authors: Pei-Shiuan Jiang, 江佩璇
• Other Authors: Gin-Der Wu
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/37299647894176686493

碩士 === 國立暨南國際大學 === 電機工程學系 === 99 === This thesis is investigated in two ways to reach enhance the recognition rates respectively, one is to combine different modes which have different advantages, and the other is cepstral statistics normalization techniques to reduce noise effect. The first part of thesis combines Linear Discriminant Analysis,(LDA), Principal Comonents Analysis,(PCA), and Minimum Classification Error,(MCE) to train model and reach robust the speech feature. The second part of thesis uses cepstral mean subtraction (CMS), cepstral mean and variance normalization (CMVN) as basic. And normalization techniques use the characteristic parameter which is speech-only to construct codebook. Normalization techniques give weight for every codebook. Then codebook and utterance information are combined in cepstral statistics for normalization. And template matching employs Gaussian Mixture Model, (GMM). Finally, this way compares and discusses the results which are tested in several variable background noises form different conditions.