一、簡介
本文基于Matlab設計實作了一個文本相關的聲紋識別系統，可以判定說話人身份，
1 系統原理
a.聲紋識別
? ? 這兩年隨著人工智能的發展，不少手機App都推出了聲紋鎖的功能，這里面所采用的主要就是聲紋識別相關的技術，聲紋識別又叫說話人識別，它和語音識別存在一點差別，
b.梅爾頻率倒譜系數（MFCC）
梅爾頻率倒譜系數（Mel Frequency Cepstrum Coefficient, MFCC）是語音信號處理中最常用的語音信號特征之一，
實驗觀測發現人耳就像一個濾波器組一樣，它只關注頻譜上某些特定的頻率，人耳的聲音頻率感知范圍在頻譜上的不遵循線性關系，而是在Mel頻域上遵循近似線性關系，
梅爾頻率倒譜系數考慮到了人類的聽覺特征，先將線性頻譜映射到基于聽覺感知的Mel非線性頻譜中，然后轉換到倒譜上，普通頻率轉換到梅爾頻率的關系式為：
c.矢量量化（VectorQuantization）
本系統利用矢量量化對提取的語音MFCC特征進行壓縮，
VectorQuantization (VQ)是一種基于塊編碼規則的有損資料壓縮方法，事實上，在 JPEG 和 MPEG-4 等多媒體壓縮格式里都有 VQ 這一步，它的基本思想是：將若干個標量資料組構成一個矢量，然后在矢量空間給以整體量化，從而壓縮了資料而不損失多少資訊，
3 系統結構
本文整個系統的結構如下圖：
? –訓練程序
首先對語音信號進行預處理，之后提取MFCC特征引數，利用矢量量化方法進行壓縮，得到說話人發音的碼本，同一說話人多次說同一內容，重復該訓練程序，最終形成一個碼本庫，
? –識別程序
在識別時，同樣先對語音信號預處理，提取MFCC特征，比較本次特征和訓練庫碼本之間的歐氏距離，當小于某個閾值，我們認定本次說話的說話人及說話內容與訓練碼本庫中的一致，配對成功，
4 測驗實驗
可以看到只有說話人及說話內容與碼本庫完全一致時才會顯示“密碼正確”，否則顯示“密碼錯誤”，實作了聲紋鎖的相關功能，
二、源代碼
function varargout = GUI(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GUI_OpeningFcn, ...
'gui_OutputFcn', @GUI_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before GUI is made visible.
function GUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% varargin command line arguments to GUI (see VARARGIN)
% Choose default command line output for GUI
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = GUI_OutputFcn(hObject, eventdata, handles)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in trainrec.
function trainrec_Callback(hObject, eventdata, handles)
speaker_id = trainrec();
set(handles.train_current,'string','Hurraay,DONE!');
speaker_iden = sprintf('you re speaker number %d', speaker_id);
% set(handles.speaker,'string',speaker_iden);
set(handles.access,'BackgroundColor','blue');
set(handles.access,'string','YOU HAVE ACCESS, TRAIN COMMANDS NOW!');
% if access_ == 1
% set(handles.access,'string','YOU HAVE ACCESS, TRAIN COMMANDS NOW!');
% else
% set(handles.access,'string','YOU DONT HAVE ACCESS,SPEAKER NOT RECOGNIZED!');
% end
% --- Executes on button press in command.
function command_Callback(hObject, eventdata, handles)
trai_pairs=30;
out_neurons=5;
hid_neurons=6;
in_nodes=13;
eata=0.1;emax=0.001;q=1;e=0;lamda=.7; t=1;
load backp.mat W V;
recObj = audiorecorder;
Fs=8000;
Nseconds = 1;
while(1)
fprintf('say any word immediately after hitting enter');
input('');
recordblocking(recObj, 1);
x = getaudiodata(recObj);
[kk,g] = lpc(x,12);
Z=(kk);
Z=double(Z);
p1=max(Z);
Z=Z/p1;
for p=1:trai_pairs
z=transpose(Z(p,:));
% calculate output
y=(tansig(V*(z)));
o=(tansig(W*(y)));
break
end
b=o(1);
c=o(2);
d=o(3);
e=o(4);
f=o(5);
a= max(o);
if (b==a )
display('AHEAD');
set(handles.ahead,'BackgroundColor','green');
set(handles.command,'string','Ahead');
pause(2);
elseif (c== a)
display('STOP');
set(handles.stop,'BackgroundColor','green');
set(handles.command,'string','Stop');
pause(2);
elseif (d== a)
display('BACK');
set(handles.back,'BackgroundColor','green');
set(handles.command,'string','Back');
pause(2);
elseif (e==a)
display('LEFT');
set(handles.left,'BackgroundColor','green');
set(handles.command,'string','Left');
pause(2);
elseif (f==a)
display('RIGHT');
set(handles.right,'BackgroundColor','green');
set(handles.command,'string','Right');
pause(2);
end
set(handles.ahead,'BackgroundColor','white');
set(handles.left,'BackgroundColor','white');
set(handles.right,'BackgroundColor','white');
set(handles.stop,'BackgroundColor','white');
set(handles.back,'BackgroundColor','white');
end
function traincommands()
Fs=8000;
Nseconds = 1;
samp=6;
words=5;
recObj = audiorecorder;
aheaddir = 'C:\Users\Rezetane\Desktop\HRI Proj\Speech-Recognition-master\data\train_commands\ahead\';
backdir = 'C:\Users\Rezetane\Desktop\HRI Proj\Speech-Recognition-master\data\train_commands\back\';
stopdir = 'C:\Users\Rezetane\Desktop\HRI Proj\Speech-Recognition-master\data\train_commands\stop\';
rightdir = 'C:\Users\Rezetane\Desktop\HRI Proj\Speech-Recognition-master\data\train_commands\right\';
leftdir = 'C:\Users\Rezetane\Desktop\HRI Proj\Speech-Recognition-master\data\train_commands\left\';
s_right = numel(dir([rightdir '*.wav']));
for i= 1:1:samp
filename = sprintf('%ss%d.wav', aheaddir, i);
fprintf('Reading %ss%d ',aheaddir,i);
[x,Fs] = audioread(filename);
[s(i,:),g] = lpc(x,12);
end
for i= (samp+1):1:2*samp
filename = sprintf('%ss%d.wav', stopdir, i- samp);
fprintf('Reading %ss%d ',stopdir,i);
[x,Fs] = audioread(filename);
[s(i,:),g] = lpc(x,12);
%plot(s(i,:));
end
for i= (2*samp+1):1:3*samp
filename = sprintf('%ss%d.wav', backdir, i-2*samp);
fprintf('Reading %ss%d ',backdir,i);
[x,Fs] = audioread(filename);
[s(i,:),g] = lpc(x,12);
end
for i= (3*samp+1):1:4*samp
filename = sprintf('%ss%d.wav', leftdir, i-3*samp);
fprintf('Reading %ss%d ',leftdir,i);
[x,Fs] = audioread(filename);
[s(i,:),g] = lpc(x,12);
end
for i= (4*samp+1):1:5*samp
filename = sprintf('%ss%d.wav', rightdir, i- 4*samp);
fprintf('Reading %ss%d ',rightdir,i);
[x,Fs] = audioread(filename);
[s(i,:),g] = lpc(x,12);
end
S=zeros(1,13);
for i=1:1:samp
S=cat(1,S,s(i,:));
S=cat(1,S,s(samp+i,:));
S=cat(1,S,s(2*samp+i,:));
S=cat(1,S,s(3*samp+i,:));
S=cat(1,S,s(4*samp+i,:));
end
S(1,:)=[];
save speechp.mat S
trai_pairs=30; % 48 samples
out_neurons=5; % no of words
hid_neurons=6; %matka
in_nodes=13; %features are 13
eata=0.1;emax=0.001;q=1;e=0;lamda=.7; t=1;
load speechp.mat S
p1=max(max(S));
s=S/p1;
Z= double(s);
dummy=[1 -1 -1 -1 -1;
-1 1 -1 -1 -1;
-1 -1 1 -1 -1;
-1 -1 -1 1 -1;
-1 -1 -1 -1 1];
t=trai_pairs/out_neurons;
D=dummy;
for i= 1:1:5
D=cat(1,D,dummy);
end三、運行結果
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