XJIPC OpenIR  > 多语种信息技术研究室
蒋同海; 齐耀辉; 葛凤培; 颜永红
Source Publication网络新媒体技术
Other AbstractIn order to give an accurate assessment,the text of test speech should be recognized firstly in text - independent pronunciation quality assessment. Real evaluation data have some disadvantageous factors which affect the correct rate of recognition,such as noise,accent,channel noise and spontaneous speaking style. In this paper we investigate these factors by improving the acoustic model of the speech recognition system. Background noise is added to the training data to enhance the ability of anti - noise. Speaker - based Cepstral Mean and Variance Normalization ( SCMVN) is adopted to alleviate the distortion of channel and the impact of inter - speaker variability. Maximum a Posteriori ( MAP) adaptation is done by using reading speech from the same region as the test data to tune acoustic model to match the pronunciation characteristic of the accent. Spontaneous speech are used to do MAP adaptation to tune acoustic model to describe the spontaneous style in spoken language. According to the experimental results,the speech recognition accuracy of word correct rate is improved relatively by 44. 1%,and the speech evaluation accuracy of correlation coefficient between machine and expert score is improved relatively by 6.3%.
Keyword文本无关发音质量评估 声学模型 Map 基于说话人的倒谱均值方差规整
Document Type期刊论文
Recommended Citation
GB/T 7714
蒋同海,齐耀辉,葛凤培,等. 文本无关发音质量评估系统中声学模型的若干研究和改进[J]. 网络新媒体技术,2012,1(2):47-53.
APA 蒋同海,齐耀辉,葛凤培,&颜永红.(2012).文本无关发音质量评估系统中声学模型的若干研究和改进.网络新媒体技术,1(2),47-53.
MLA 蒋同海,et al."文本无关发音质量评估系统中声学模型的若干研究和改进".网络新媒体技术 1.2(2012):47-53.
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