搜索资源列表
HTK-3.3
- Hidden Markov Toolkit (HTK) 3.2.1 HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering Department (ht
HTK-3.3
- Hidden Markov Toolkit (HTK) 3.2.1 HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering Department (ht
TTS_Reader
- Text to Speech with the Microsoft Speech Library and SDK version 5.1-Text to Speech with the Microsoft Speech Li brary and SDK version 5.1
TTS1
- TTS.TTS是text-to-speech的缩写，英文也称Speech Synthesis即语音合成. Microsoft Visual Studio2005+Windows2000/XP+ Microsoft Speech SDK 5.1+Microsoft Speech SDK 5.1 Language Pack 功能说明： 按课或级分类单词，用文件或数据库保存，用户可根据需要选择对应课或级别中的单词； 从文件或数
Peak_SNR
- this program aim to Evaluate the speech quality of speec signal it can calculate SR PNR
Short-time-magnitude-short-time-energy-plot-speec
- Plots short time magnitude and short time energy of given speech signals. Explanations is in the file.
Source-cell-phone-recognition-from-recorded-speec
- Source cell-phone recognition recorded speech using non-speech segements-Source cell-phone recognition recorded speech using non-speech segements
Kalman-speec-hmatlab-code-enhancement-corrupted-w
- A matlab code for speech enhancement using Kalman filtering. Speech corrupted with multi talker babble noise.
Kalman-speec-hmatlab-code-enhancement-corrupted-w
- A matlab code for speech enhancement using Kalman filtering. Speech corrupted with car inside noise travelling at 60kmph with windows closed.
Kalman-speec-hmatlab-code-enhancement-corrupted-w
- A matlab code for enhancement of speech using Kalman filtering. Speech corrupted with exhaust fan noise.
Kalman-speec-hmatlab-code-enhancement-corrupted-w
- A matlab code for speech enhancement using Kalman filtering. Speech corrupted with street noise while sitting inside an auto rickshaw.
Speech Encoding - Frequency Analysis MATLAB
- 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 p