搜索资源列表
dbdoor
- 双门限算法，用于语音端点检测。可以通过调整门限值，并加入门限自适应算法，实现语音端点检测。-double door ,speech endpoint detection two games adjust itself
recognition
- 语音识别的端点检测工具,包括预处理，预加重，mfcc，端点检测。-Endpoint detection of speech recognition tools, including pretreatment, pre-emphasis, mfcc, endpoint detection.
Wave
- 好好用的端点检测工具，可以单独性的把端点检测出来，进行下一步的mfcc步骤。是很好的语音识别工具。-Endpoint detection tool to good use, you can separate out of the endpoint detection, the next step of mfcc step. Is a very good speech recognition tools.
vad
- 用于语音端点检测，可以将连续语音分割为孤立词。-For voice activity detection, continuous speech can be split into isolated words.
specbandvar_SingleVoice
- 基于频率方差和子带分离的语音端点检测程序，基于matlab开发-based on specband var Vad detection programe,using matlab
taep_end_detection
- 本程序主要是利用利用语音信号的基本特性进行谱熵端点检测-taep end detection
nengliang1
- 语音端点检测，门限法包括短时能量、过零率-Voice activity detection, threshold laws, including the short-term energy, zero crossing rate, etc.
voice_prcossing
- 语音增强，倒谱，端点检测，共振峰检测，去噪-Speech enhancement, cepstrum, endpoint detection, formant detection, denoising
LPCC_MFCC_VAD
- MFCC特征提取算法以及语音端点检测源码-MFCC feature extraction algorithm and voice activity detection source
duandianjiance
- 用于语音信号的端点检测，如有研究有关语音信号识别的大虾们可以过来-Endpoint detection for speech signals, if any, to study the speech signal recognition can come back to the prawns
enframe
- 加窗技术用于端点检测技术 分析语音信号-Window technology for endpoint detection
mfcc
- 通过预处理、端点检测、mfcc的方法进行语音识别-It s about speech recognition
endpointdetecting(matlab)
- 语音端点检测，有助于除去语音信号中的噪音部分，留下有用的携带信息的部分，有利于提取其中有用的语音特征参数，从而提高识别率。-Endpoint detection, helps remove the noise part of the speech signal, leaving useful information-bearing part, is conducive to extract the speech feature useful to improve the recognition
Speechrecognitiontechnology
- 比较详尽的介绍了语音识别系统的实现过程，以及相关技术。 端点检测：基于短时能量和短时平均过零率的端点检测和基于倒谱特征的端点检测 特征参数提取：LPCC和MFCC 参数模板存储：HMM和N_Gram 识别阶段：DWT 各阶段的相关技术都给了详细的介绍，绝对是好东西！-More detailed introduction to the speech recognition system implementation process and related technologie
mfccdtw
- 先用端点检测将语音中有用的语音部分提取出来（即将头部和尾部的静音部分除掉），然后用LPC算法提取语音信号的特征参数，进行动态归整(DTW算法)后与模板库里面的标准语音作比较，最后将识别结果进行D/A转化后播放出来。在本部分的设计中，则主要完成语音识别的模式匹配算法部分的软件实现。 -First with the endpoint detection of speech to voice some of the useful extracted from the (soon to mute som
speech
- 这是一段语音识别的c++源程序，包括预处理，端点检测，线性倒谱系数，dtw算法模式匹配。-This is a speech recognition c++ source, including preprocessing, detection, linear cepstrum, dtw algorithm for pattern matching.
endpoint
- 语音端点检测程序，利用均方能量算法逐帧进行判断-Voice activity detection procedure, and use the energy mean square algorithm
Endpoindetectionspectralenhancement
- 基于端点检测的谱减法语音增强，语音的去噪算法仿真程序，可以进一步改进-Endpoint detection of the spectral subtraction based speech enhancement
edp
- 这个是端点检测的程序，用的是基于短时能量和过零率的方法，最后在语音信号的波形图上对检测到的端点进行了标注-This is the endpoint detection process, using a short-term energy and zero-based rate method, the last waveform in the speech signal detected on the endpoint to the mark
VADEndPointDetection
- 活动语音端点检测的程序，在较强背景噪音下，具有较好的效果-Voice activity detection process, under the strong background noise, with good results