【语音识别】基于DTW算法实现0~9数字和汉字语音识别含Matlab源码

1 简介

在孤立词语音识别中,动态时间规整DTW算法是一种应用较为广泛的算法之一,有着较强的科学性,在立足于当前DTW语音识别算法应用的实际情况下,简略阐述了该课题的研究背景,并从预处理和特征参数提取以及DTW算法两方面着手对基于DTW算法的语音识别系统实现进行了探究,以此为基础展开了相应的仿真和分析,旨在为相关研究人员提供参考.

语音识别系统的典型原理框图如图1-1所示。从图中可以看出语音识别系统的本质就是一种模式识别系统，它也包括特征提取、模式匹配、参考模式库等基本单元。由于语音信号是一种典型的非平稳信号，加之呼吸气流、外部噪音、电流干扰等使得语音信号不能直接用于提取特征，而要进行前期的预处理。预处理过程包括预滤波、采样和量化、分帧、加窗、预加重、端点检测等。经过预处理的语音数据就可以进行特征参数提取。在训练阶段，将特征参数进行一定的处理之后，为每个词条得到一个模型，保存为模板库。在识别阶段，语音信号经过相同的通道得到语音参数，生成测试模板，与参考模板进行匹配，将匹配分数最高的参考模板作为识别结果。后续的处理过程还可能包括更高层次的词法、句法和文法处理等，从而最终将输入的语音信号转变成文本或命令。

​本文所描述的语音识别系统(下称本系统)将对数字0~9共10段参考语音进行训练并建立模板库，之后将对多段测试语音进行识别测试。系统实现了上图中的语音输入、预处理、特征提取、训练建立模板库和识别等模块，最终建立了一个比较完整的语音识别系统。

2 部分代码

function [x,mn,mx]=melbankm(p,n,fs,fl,fh,w)%MELBANKM determine matrix for a mel-spaced filterbank [X,MN,MX]=(P,N,FS,FL,FH,W)%% Inputs:  p   number of filters in filterbank%    n   length of fft%    fs  sample rate in Hz%    fl  low end of the lowest filter as a fraction of fs (default = 0)%    fh  high end of highest filter as a fraction of fs (default = 0.5)%    w   any sensible combination of the following:%          't'  triangular shaped filters in mel domain (default)%          'n'  hanning shaped filters in mel domain%          'm'  hamming shaped filters in mel domain%%          'z'  highest and lowest filters taper down to zero (default)%          'y'  lowest filter remains at 1 down to 0 frequency and%         highest filter remains at 1 up to nyquist freqency%%           If 'ty' or 'ny' is specified, the total power in the fft is preserved.%% Outputs:  x     a sparse matrix containing the filterbank amplitudes%          If x is the only output argument then size(x)=[p,1+floor(n/2)]%          otherwise size(x)=[p,mx-mn+1]%    mn   the lowest fft bin with a non-zero coefficient%    mx   the highest fft bin with a non-zero coefficient%% Usage:  f=fft(s);      f=fft(s);%    x=melbankm(p,n,fs);    [x,na,nb]=melbankm(p,n,fs);%    n2=1+floor(n/2);    z=log(x*(f(na:nb)).*conj(f(na:nb)));%    z=log(x*abs(f(1:n2)).^2);%    c=dct(z); c(1)=[];%% To plot filterbanks e.g.  plot(melbankm(20,256,8000)')%​​%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%   This program is free software; you can redistribute it and/or modify%   it under the terms of the GNU General Public License as published by%   the Free Software Foundation; either version 2 of the License, or%   (at your option) any later version.%%   This program is distributed in the hope that it will be useful,%   but WITHOUT ANY WARRANTY; without even the implied warranty of%   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the%   GNU General Public License for more details.%%   You can obtain a copy of the GNU General Public License from%   ftp://prep.ai.mit.edu/pub/gnu/COPYING-2.0 or by writing to%   Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%​if nargin < 6  w='tz';  if nargin < 5    fh=0.5;    if nargin < 4      fl=0;    end  endendf0=700/fs;fn2=floor(n/2);lr=log((f0+fh)/(f0+fl))/(p+1);% convert to fft bin numbers with 0 for DC termbl=n*((f0+fl)*exp([0 1 p p+1]*lr)-f0);b2=ceil(bl(2));b3=floor(bl(3));if any(w=='y')  pf=log((f0+(b2:b3)/n)/(f0+fl))/lr;  fp=floor(pf);  r=[ones(1,b2) fp fp+1 p*ones(1,fn2-b3)];  c=[1:b3+1 b2+1:fn2+1];  v=2*[0.5 ones(1,b2-1) 1-pf+fp pf-fp ones(1,fn2-b3-1) 0.5];  mn=1;  mx=fn2+1;else  b1=floor(bl(1))+1;  b4=min(fn2,ceil(bl(4)))-1;  pf=log((f0+(b1:b4)/n)/(f0+fl))/lr;  fp=floor(pf);  pm=pf-fp;  k2=b2-b1+1;  k3=b3-b1+1;  k4=b4-b1+1;  r=[fp(k2:k4) 1+fp(1:k3)];  c=[k2:k4 1:k3];  v=2*[1-pm(k2:k4) pm(1:k3)];  mn=b1+1;  mx=b4+1;endif any(w=='n')  v=1-cos(v*pi/2);elseif any(w=='m')  v=1-0.92/1.08*cos(v*pi/2);endif nargout > 1  x=sparse(r,c,v);else  x=sparse(r,c+mn-1,v,p,1+fn2);end​

3 仿真结果

4 参考文献

[1]吴晓平, 崔光照, 路康. 基于DTW算法的语音识别系统实现[J]. 信息化研究, 2004(07):17-19.

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