Description: : In order to stress variation in the robustness of speech recognition system can achieve better recognition results, based on Hidden Markov Model (HMM) of adaptive technology, put forward a maximum a posteriori probability (MAP) and Maximum Likelihood regression (MLLR) for the stress of the adaptive variation in voice. The experimental results show that compared with the basic system, both methods are effective to improve the system recognition rate. SD as the initial model to the maximum a posteriori probability method in 150 training samples to identify the best, can reach 90.4 .
- [HMM(camb).Rar] - a Cambridge on HMM good Matlab prepared
- [gaussianSrc] - The EM algorithm is short for Expectatio
- [ML_and_MAP] - Maximum Likelihood (ML) criteria and max
- [MAPAlgorithm] - This program includes: [5 7] convolution
- [5] - speech paper，help you study
- [ML] - The algorithm is a classic signal to noi
- [4] - MAP-based infrared image super-resolutio
- [boundsonLDPCandLDGM] - The paper is about bounds for LDPC and L
- [yizhenzhouqi] - zi xiang guan ti qu ji yin zhou qi
- [mllr] - Speech recognition maximum likelihood li
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