An Improved HMM Speech Recognition Model
In order to overcome the defects of the duration modeling of homogeneous HMM in speech recognition and the unreaistic assumption that successive observations are independent and identically distribution within a state, Markov Family model (MFM), a new statistical model is proposed in this paper. Independence assumption is placed by conditional independence assumption in Markov Family model. We have successfully applied Markov Family model to speech recognition and propose duration distribution based MFM recognition model (DDBMFM) which takes duration distribution into account and integrates the frame and segment based acoustic modeling techniques. The speaker independent continuous speech recognition experiments show that this new recognition model have higher performance than standard HMM recognition models.
Lichi Yuan
School of Information Technology, Jiangxi University of Finance & Economics, Nanchang, 330013, China College of information Science and Engineering, Central South University, Changsha 410083, China
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
1311-1315
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）