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用Matlab实现DTW孤立词识别
做语音识别时用到DTW 算法。便于初学者的学习。(DTW algorithm is used for speech recognition. Easy for beginners to learn.)
- 2020-12-07 12:39:22下载
- 积分：1
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VQ ASR
基于VQ的说话人识别系统，在MATLAB环境下实现基于矢量量化的说话人识别系统。在实时录音的情况下，利用该说话人识别系统，对不同的人的1s～7s的语音进行辨识。实现与文本无关的自动说话人确认的实时识别。(speaker recognition system based on vector quantization)
- 2019-06-07 12:44:37下载
- 积分：1
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beamforming1
语音信号处理，阵列为10阵元线阵，宽带和窄带波束形成(Speech signal processing, 10 sensors of microphone linear array,Broadband and narrowband beamforming)
- 2018-05-10 15:30:57下载
- 积分：1
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yuchuli
一种用于语音或者心音去噪和提取包络的预处理方法，对于后面信号处理更加方便(A method for denoising and extract voice or heart sounds envelope pretreatment method, the signal processing is more convenient for the latter)
- 2013-10-30 14:32:33下载
- 积分：1
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Speech
语音识别功能，可以直接说话进行识别，然后转换成文字(Speech recognition)
- 2013-01-29 20:27:08下载
- 积分：1
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yycl
提取语音信号的lpc参数并进行时间归整，需要将wav语音文件放在指定目录下‘e:yyzl’(voice signal from the lpc parameters and time consolidation, need to wav sound files on the specified directory 'e : yyzl')
- 2006-11-28 12:11:09下载
- 积分：1
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Td-psolaPitchDuration
使用TD-PSOLA（td-psola、psola）算法，做了一个语音合成，实现语音声调的变调，时长的变化等功能。参考praat源码，提取出其合成方法，效果不错。需要提供pitchtier、durationtier、pointprocess等准换信息文本（可由praat产生）供readfile函数读入(Use TD-PSOLA (TD-PSOLA, PSOLA algorithm, made a speech synthesis, speech tone tone sandhi, long-term changes in the function. Reference Praat source code, extract its synthetic method, the effect of the same.)
- 2021-03-13 14:59:24下载
- 积分：1
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speech_vad
利用MATLAB来实现语音的分帧及语音端点检测(Using MATLAB to achieve voice segmentation and voice endpoint detection)
- 2017-07-12 21:44:14下载
- 积分：1
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svm-based-tone-recognition
基于svm分类器的汉语声调识别，实现了对声调四声的识别，纯净语音识别率100 ，噪声环境下识别率90 (The svm classifier based Chinese tone recognition, the four tones of tone recognition, the pure voice recognition rate of 100 , 90 recognition rate in noisy environments)
- 2013-01-23 11:02:47下载
- 积分：1
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ABSE
熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)
- 2020-11-02 21:29:54下载
- 积分：1
