Description: 语音识别系统中前端PLP参数的提取和处理
孤立词识别系统中几种滤波器组的比较研究
基于Matlab命令字识别系统及实现
基于HTK的命令字识别系统及实现
基于HTK的数字串识别系统及实xian-Speech Recognition System front-end PLP parameters for the extraction and processing isolated word recognition system filters The comparative study based on Matlab command word recognition system based HTK and the command word recognition system and the HTK-based digital identification systems and string it Xian Platform: |
Size: 23953493 |
Author:guoanjia |
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Description: 语音识别系统中前端PLP参数的提取和处理
孤立词识别系统中几种滤波器组的比较研究
基于Matlab命令字识别系统及实现
基于HTK的命令字识别系统及实现
基于HTK的数字串识别系统及实xian-Speech Recognition System front-end PLP parameters for the extraction and processing isolated word recognition system filters The comparative study based on Matlab command word recognition system based HTK and the command word recognition system and the HTK-based digital identification systems and string it Xian Platform: |
Size: 23953408 |
Author:guoanjia |
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Description: 这个算法用于语音识别的前端处理，效果非常好-The algorithm used for speech recognition front-end treatment, the effect is very good Platform: |
Size: 3072 |
Author:张强 |
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Description: 关于语音处理的英文书籍，其中特征提取部分（MFCC）讲解的很好很详细-The performance of speech recognition systems receiving speech that has been transmitted over mobile channels can be
significantly degraded when compared to using an unmodified signal. The degradations are as a result of both the low
bit rate speech coding and channel transmission errors. A Distributed Speech Recognition (DSR) system overcomes
these problems by eliminating the speech channel and instead using an error protected data channel to send a
parameterized representation of the speech, which is suitable for recognition. The processing is distributed between the
terminal and the network. The terminal performs the feature parameter extraction, or the front-end of the speech
recognition system. These features are transmitted over a data channel to a remote "back-end" recognizer. The end result
is that the transmission channel does not affect the recognition system performance and channel invariability is achieved. Platform: |
Size: 101376 |
Author:gqy |
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Description: This file contains the main part of DSR front-end. Speech samples
* are read from input waveform file frame by frame. Feature
* extraction is performed for each frame by calling basic
* functions of the basic FE function package (see FEfunc.c).
* Feature vectors are output to file in HTK format.
* Command line arguments are handled by a command line parsing
* function.-This file contains the main part of DSR front-end. Speech samples
* are read from input waveform file frame by frame. Feature
* extraction is performed for each frame by calling basic
* functions of the basic FE function package (see FEfunc.c).
* Feature vectors are output to file in HTK format.
* Command line arguments are handled by a command line parsing
* function. Platform: |
Size: 18432 |
Author:KIMHOON |
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Description: 用于语音识别前端信号预处理的快速傅里叶变换，提高语音信号识别率！-Fast Fourier transform for speech recognition front-end signal pre-processing, improve the recognition rate of voice signal Platform: |
Size: 1024 |
Author:尚少锋 |
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Description: 分布式语音识别（DSR）是为了减轻语音处理前端的运算压力而产生的一种系统化的语音处理流程，目前欧洲电信标准化协会（ETSI）已经发布了多个版本的DSR标准C代码。本代码利用C67系列DSP芯片的特性，对ETSI标准DSR代码进行了优化。可以在ccs studio平台下编译运行。-Distributed Speech Recognition is a systemized speech processing architecture, that is used to unburden the front end. This code implements the ETSI DSR standard, and can be compiled under CCS studio environment. Platform: |
Size: 790528 |
Author:Zhang |
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Description: In this paper, we figure out the use of appended jitter and shimmer speech features for closed set text
independent speaker identification system. Jitter and shimmer features are extracted from the
fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency
Cepstral Coefficients (MFCCs) for human speech and MFCC-GC which integrate the Gammachirp
filterbank instead of the Mel scale. Hidden Markov Models (HMMs) with Gaussian Mixture Models
(GMMs) state distributions are used for classification. Our approach achieves substantial performance
improvement in a speaker identification task compared with a state-of-the-art robust front-end in a
clean condition. Platform: |
Size: 256000 |
Author:mansouri |
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Description: Signal processing front end for extracting the feature
set is an important stage in any speech recognition
system. The optimum feature set is still not yet
decided. There are many types of features, which are
derived differently and have good impact on the
recognition rate. This paper presents one more
successful technique to extract the feature set a
speech signal, which can be used in speech recognition
systems. Our technique based on the human auditory
system characteristics and relies on the gammachirp
filterbank to emulate asymmetric frequency response
and level dependent frequency response.-Signal processing front end for extracting the feature
set is an important stage in any speech recognition
system. The optimum feature set is still not yet
decided. There are many types of features, which are
derived differently and have good impact on the
recognition rate. This paper presents one more
successful technique to extract the feature set a
speech signal, which can be used in speech recognition
systems. Our technique based on the human auditory
system characteristics and relies on the gammachirp
filterbank to emulate asymmetric frequency response
and level dependent frequency response. Platform: |
Size: 544768 |
Author:student |
Hits:
Search - front end speech