FastICA

完整的基于HMM的语音识别系统，时间序列数据分析中的梅林变换工具，最终的权值矩阵就是滤波器的系数。( Complete HMM-based speech recognition system, Time series data analysis Mellin transform tool, The final weight matrix is ??the filter coefficient.)

语音信号的时域、频域分析,包括短时能量分析、短时平均过零率、自相关函数、短时平均幅度差函数等。(Time-domain speech signal, frequency domain analysis, including short-term energy analysis, the average short-term zero-crossing rate, autocorrelation function, such as short-time average magnitude difference function.)

利用mfcc特性进行语音识别，分类算法为svm。(Speech recognition using MFCC characteristics)

C / winform_ multiple forms transfer data operations between each othe

源－目标说话人声音转换的学位论文：源－目标说话人声音转换则是指将源说话人的语音模式转换成某个指定的特定目标说话人的语音模式，即保持源说话人原有的语音信息内容不变，使转换后的语音具有目标说话人的声音特点。(Source- target speaker voice conversion refers to the source speaker voice patterns into a designated target speaker-specific speech patterns, that is, the source speaker to keep the original voice message unchanged, so that the converted speech with the goal of the speaker s voice characteristics.)

一种关于倒谱的实现研究处理方法的总结以及展望(One kind of research on cepstrum processing method to achieve a summary and outlook)

C语言实现的端点检测,具有良好的效果,对于做语音识别者来说，是很好的参考(C language implementation of endpoint detection, with good results)

计算语音、音频信号的信噪比程序（包含音频样本文件）(Calculation of voice, audio program signal to noise ratio (including the audio sample file))

本文主要研究普通计算环境下语音自动识别(ASR)技术的软件实时实现，基于语音识别的基本原理和过程。在VC环境下建立了一套语音识别系统及其演示软件。实验表明，该系统对特定人情况有较高的识别率和一定的实时性(In this paper, under the computing environment on general automatic speech recognition (ASR) technology to achieve real-time software, speech recognition based on the basic principles and processes. VC environment in the establishment of a voice recognition system and its presentation software. The experiments show that the systems are in particular have a high recognition rate and a certain degree of real-time)

完整的基音识别程序,经过修改,测试正确。包含基音检测，DTW，LPCC，MFCC提取等(integrity of the pitch identification procedures, the result of the revision, the test correctly. Pitch Detection included, DTW, LPCC Features, MFCC extraction)