基于匹配滤波器的语音识别。本程序接触匹配滤波对加噪语音进行匹配输出，使得信号的信噪比得到提升，送入后端DTW的语音识别系统，实验证明，本程序对语音提升有很大的帮助
A speech recoginiton base on the matched filter. this code will use matched filter and get speech SNR output enhanced. then we will extrace MFCC, send the feature to the DTW recongition system, the test result verify that this method is good for speech recoginition SNR enhanced. (2015-01-10, matlab, 4547KB, 下载16次)
论文首先介绍了传统的语音特征参数MFCC，它是基于人耳听觉
特性设计的一种特征参数，在静音环境下能得到较高的识别率，但在
信噪比较低时识别率急剧下降，不利于实用化。本文通过对MFCC算
法的分析和研究，发现其中的FFT和DCT在整个时频空间使用固定的
。分析窗，这不符合语音信号特性，而小波变换具有多分辨率特性，更
符合人耳的听觉特性。因此，本文将小波变换和MFCC算法相结合，
提出了三种新的语音识别特
Speech recognition has wide use in the field of communication and so
on·Speech feature parameter extraction is an important part of the speech
recognition system．The performance of the feature parameter haftuences
the system’S performance directly．And the environmental noise is a kev
factor of restricting the performance of the feature parameter．This paper，s
research object is extracting the speech feature parameter under the noise
environment·Then it analyzes the auditory model of human，deeply
analyzes and researches the traditional speech feature parameter MFCC，
and proposes three improved MFCC parameters based on theⅥ，avelet
transformation and the auditory characteristic ofhuman．Besides，the paper
also gives all improved method about the feature parameter ZCPA． (2011-08-13, matlab, 1955KB, 下载21次)
利用I.Cohen提出的短时先验信噪比（priori SNR）估算的语音增强法，附有相应程序和文献。
I. Cohen raised by the use of short-term a priori signal to noise ratio (priori SNR) estimation of the speech enhancement method, with the corresponding procedures and documentation. (2008-11-23, matlab, 123KB, 下载133次)
为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列 ，其均值为零；参考信号 ；信道具有脉冲响应： 式中w用来控制信道的幅度失真(w = 2～4，例如，取w = 2.9,3.1,3.3,3.5等),而且信道受到均值为零、方差为 (例如，取 ，相当于信噪比为30dB)的高斯白噪声 的干扰。试比较基于下列五种算法自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况，在同一坐标下画出其学习曲线)： 横向/格-梯型结构LMS算法[1-4] 横向/格-梯型结构RLS算法[1-4] (2007-09-12, matlab, 1KB, 下载108次)
http://www.pudn.com/Download/item/id/332022.html
语音端点检测是语音识别中至关重要的技术。无论军用还是民用，语音端点检测都有着广泛的应用。在低信噪比的环境中进行精确的端点检测比较困难，尤其是在无声段或者发音前后
voice activity detection is critical speech recognition technologies. Whether military or civilian, voice endpoint detection have broad application. Low signal-to-noise ratio in the environment for accurate endpoint detection more difficult, especially in or pronunciation of the silent before and after (2007-05-18, matlab, 519KB, 下载153次)