【语音识别】基于MFCC实现声纹识别matlab源码

一、简介

本文基于Matlab设计实现了一个文本相关的声纹识别系统，可以判定说话人身份。
1 系统原理
a.声纹识别
这两年随着人工智能的发展，不少手机App都推出了声纹锁的功能。这里面所采用的主要就是声纹识别相关的技术。声纹识别又叫说话人识别，它和语音识别存在一点差别。

b.梅尔频率倒谱系数（MFCC）
梅尔频率倒谱系数（Mel Frequency Cepstrum Coefficient, MFCC）是语音信号处理中最常用的语音信号特征之一。
实验观测发现人耳就像一个滤波器组一样，它只关注频谱上某些特定的频率。人耳的声音频率感知范围在频谱上的不遵循线性关系，而是在Mel频域上遵循近似线性关系。
梅尔频率倒谱系数考虑到了人类的听觉特征，先将线性频谱映射到基于听觉感知的Mel非线性频谱中，然后转换到倒谱上。普通频率转换到梅尔频率的关系式为：

c.矢量量化（VectorQuantization）
本系统利用矢量量化对提取的语音MFCC特征进行压缩。
VectorQuantization (VQ)是一种基于块编码规则的有损数据压缩方法。事实上，在 JPEG 和 MPEG-4 等多媒体压缩格式里都有 VQ 这一步。它的基本思想是：将若干个标量数据组构成一个矢量，然后在矢量空间给以整体量化，从而压缩了数据而不损失多少信息。
3 系统结构
本文整个系统的结构如下图：
–训练过程
首先对语音信号进行预处理，之后提取MFCC特征参数，利用矢量量化方法进行压缩，得到说话人发音的码本。同一说话人多次说同一内容，重复该训练过程，最终形成一个码本库。
–识别过程
在识别时，同样先对语音信号预处理，提取MFCC特征，比较本次特征和训练库码本之间的欧氏距离。当小于某个阈值，我们认定本次说话的说话人及说话内容与训练码本库中的一致，配对成功。

二、源代码

function varargout = test4(varargin)
% TEST4 MATLAB code for test4.fig
%      TEST4, by itself, creates a new TEST4 or raises the existing
%      singleton*.
%
%      H = TEST4 returns the handle to a new TEST4 or the handle to
%      the existing singleton*.
%
%      TEST4('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in TEST4.M with the given input arguments.
%
%      TEST4('Property','Value',...) creates a new TEST4 or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before test4_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to test4_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES% Edit the above text to modify the response to help test4% Last Modified by GUIDE v2.5 17-Mar-2019 09:58:00% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...'gui_Singleton',  gui_Singleton, ...'gui_OpeningFcn', @test4_OpeningFcn, ...'gui_OutputFcn',  @test4_OutputFcn, ...'gui_LayoutFcn',  [] , ...'gui_Callback',   []);
if nargin && ischar(varargin{1})gui_State.gui_Callback = str2func(varargin{1});
endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
elsegui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT% --- Executes just before test4 is made visible.
function test4_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to test4 (see VARARGIN)% Choose default command line output for test4
handles.output = hObject;% Update handles structure
guidata(hObject, handles);% UIWAIT makes test4 wait for user response (see UIRESUME)
% uiwait(handles.figure1);% --- Outputs from this function are returned to the command line.
function varargout = test4_OutputFcn(hObject, eventdata, handles)
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)% Get default command line output from handles structure
varargout{1} = handles.output;% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
global thk1 thk2 thk3
global tlc1 tlc2 tlc3
global tlyy1 tlyy2 tlyy3
global tqs1 tqs2 tqs3
global tyqc1 tyqc2 tyqc3
global startpos lenstartpos=601;
len=399;
[s,fs]=audioread('训练样本hk1.wav');
thk1= MFCC2par(s,fs);
thk1=thk1(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本hk2.wav');
thk2= MFCC2par(s,fs);
thk2=thk2(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本hk3.wav');
thk3= MFCC2par(s,fs);
thk3=thk3(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本lc1.wav');
tlc1= MFCC2par(s,fs);
tlc1=tlc1(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本lc2.wav');
tlc2= MFCC2par(s,fs);
tlc2=tlc2(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本lc3.wav');
tlc3= MFCC2par(s,fs);
tlc3=tlc3(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本lyy1.wav');
tlyy1= MFCC2par(s,fs);
tlyy1=tlyy1(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本lyy2.wav');
tlyy2= MFCC2par(s,fs);
tlyy2=tlyy2(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本lyy3.wav');
tlyy3= MFCC2par(s,fs);
tlyy3=tlyy3(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本qs1.wav');
tqs1= MFCC2par(s,fs);
tqs1=tqs1(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本qs2.wav');
tqs2= MFCC2par(s,fs);
tqs2=tqs2(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本qs3.wav');
tqs3= MFCC2par(s,fs);
tqs3=tqs3(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本yqc1.wav');
tyqc1= MFCC2par(s,fs);
tyqc1=tyqc1(startpos:startpos+len,1:12);[s,fs]=audioread('训练样本yqc2.wav');
tyqc2= MFCC2par(s,fs);
tyqc2=tyqc2(startpos:startpos+len,1:12);
function getmfcc= MFCC2par( x,fs)%=========================================================% 无去噪及端点检测% Input:音频数据x,采样率fs% Output：(N,M)大小的特征参数矩阵  其中N为分帧个数，M为特征维度% 特征参数：M=24 倒谱系数12维，一阶差分12维%=========================================================%[x fs]=wavread(sound);
%取单声道信号
[~,etmp]=size(x);
if (etmp==2)
x=x(:,1);
end%归一化mel滤波器组系数bank=melbankm(24,256,fs,0,0.5,'m');%Mel滤波器的阶数为24，fft变换的长度为256，采样频率为8000Hz  bank=full(bank);bank=bank/max(bank(:));%[24*129]%设定DCT系数for k=1:12n=0:23;dctcoef(k,:)=cos((2*n+1)*k*pi/(2*24));end%归一化倒谱提升窗口w=1+6*sin(pi*[1:12]./12);w=w/max(w);%预加重滤波器xx=double(x);xx=filter([1-0.9375],1,xx);%预加重xx=enframe(xx,256,80);%对x 256点分为一帧%计算每帧的MFCC参数for i=1:size(xx,1)y=xx(i,:);%取一帧数据s=y'.*hamming(256);t=abs(fft(s));%fft快速傅立叶变换  幅度谱t=t.^2; %能量谱%对fft参数进行mel滤波取对数再计算倒谱
c1=dctcoef*log(bank*t(1:129));%对能量谱滤波及DCT %t(1:129)对一帧的前128个数（帧移为128）c2=c1.*w';%归一化倒谱%mfcc参数m(i,:)=c2';end

三、运行结果