Search - hmm isolated word matlab

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[Speech/Voice recognition/combine] VAD-DTW-HMM DL : 0: 端点检测和基于DTW和HMM的孤立词识别和连续语音识别-endpoint detection and DTW-based HMM and isolated word recognition and Continuous Speech Recognition
Update : 2025-02-19 Size : 531kb Publisher : 夏洪他
[Speech/Voice recognition/combine] HMM1 DL : 0: 一个HMM的Matlab实现方法，可实现孤立词语音识别-The Matlab realize a HMM method can realize an isolated word speech recognition
Update : 2025-02-19 Size : 799kb Publisher : 陈楠
[Speech/Voice recognition/combine] hmm DL : 0: 实现mffc求解并且可利用hmm进行孤立词识别-Solving mffc realize and can be used for isolated word recognition hmm
Update : 2025-02-19 Size : 6kb Publisher : 王帝
[Speech/Voice recognition/combine] CDHMM DL : 0: 基于小波包的孤立词语音识别技术，构建了一个关于方向信息的孤立词非特定人语音识别系统。给出了从模型训练到识别的实现过程。-Based on Wavelet Packet isolated word speech recognition technology, to build an information on the direction of non-specific people isolated word speech recognition system. From the model are given training to realize the process of identification.
Update : 2025-02-19 Size : 17.91mb Publisher : wangyan
[Speech/Voice recognition/combine] 7c2f6c56ed6d DL : 0: 一个HMM的Matlab实现方法，可实现孤立词语音识别-Matlab implementation of a HMM methods, an isolated word speech recognition can be achieved
Update : 2025-02-19 Size : 799kb Publisher : 陈子秋
[matlab] HMM_glc DL : 0: 基于HMM的孤立词识别相关说明及MATLAB源码-Identifying relevant instructions and MATLAB isolated word HMM-based source
Update : 2025-02-19 Size : 14kb Publisher : 王一
[Speech/Voice recognition/combine] Speech Encoding - Frequency Analysis MATLAB DL : 0: The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov model (HMM). Consider there are n states in the HMM. The particular isolated speech signal is divided into finite number of frames. Every frame of the speech signal is assumed to be generated from any one of the n states. Each state is modeled as the multivariate Gaussian density function with the specified mean vector and the covariance matrix. Let the speech segment for the particular isolated word is represented as vector S. The vector S is divided into finite number of frames (say M). The i th frame is represented as Si . Every frame is generated by any of the n states with the specified probability computed using the corresponding multivariate Gaussian density model.
Update : 2025-02-19 Size : 769kb Publisher : Khan17