vb example of the Speech Recognition

MFCC 参数和 LPCC 参数是说话人识别中两种最常用的特征参数，研究了 MFCC 和 LPCC 参数提取的算法原理及差分倒谱参数的提取方法，采用MFCC、LPCC 及其一阶、二阶差分作为特征参数，通过 k 均值算法与三层BP 神经网络来进行说话人识别。实验结果表明，该方法可以有效提高识别率，同时也验证 MFCC 参数的鲁棒性优于 LPCC 参数

在线签名鉴定，用AR实现算法和DTW算法实现，还包括一个比较综合的实例，对此方面研究的朋友有用处-Online signature identification, use AR to achieve algorithm and DTW algorithm, but also a more comprehensive example of this research useful friends

回音消除算法matlab,采用的VIRE的双方通话检测-Matlab algorithms eliminate echoes, the two sides call the VIRE Detection

用PB写的呼叫中心-call center

提供了segmantal SNR及IS的计算代码，用于语音增强后评价用。-Provided segmantal SNR and IS calculation code for speech enhancement with post-evaluation.

不定长信号合并，％执行信号相加： y(n) = x1(n)+x2(n) ％调用方式：[y,n] = sigadd(x1,n1,x2,n2) ％ y：叠加后的信号序列的序列号，包括n1和n2 ％ x1： 第一个信号序列，序列号为n1 ％ x2：第二个信号序列，序列号为n2(n2可以与n1不同)-Indefinite long-Signal merger, add up to the implementation of signal: y (n) = x1 (n)+ X2 (n) call the way: [y, n] = sigadd (x1, n1, x2, n2) y: Superposition signal sequence after the serial number, including the n1 and n2 x1: the first signal sequence, the serial number for n1 x2: the second signal sequence, the serial number for n2 (n2 with n1 different)

一个很好的模式识别应用书籍,介绍人脸识别以及语音识别的技术-a good pattern recognition applications books, introduced face recognition and voice recognition technology

文章详尽论述了一种语音增强的新方法,应该对大家有一些启发-The article discusses in detail a new method for speech enhancement, it should be for all of us shed some light on

极小边际熵等价于叉四阶累积量的平方和最小。通过迭代使四阶累积矩阵对角化，实现交叉四阶累积量的平方和的极小化。他是语音识别的重要预处理算法-minimum entropy equivalent to the marginal four bands fork cumulative amount of square and smallest. Through iterative four bands so that the cumulative matrix diagonalization, four bands to achieve cross-cumulative amount of square and the minimization. He is an important voice recognition algorithm pretreatment

关于语音识别和语音合成的代码。 可以试试啊-On speech recognition and voice synthesis code. Try ah