Speaker Recognition System
Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker’s voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments).
The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients. Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.
Index Terms: speaker, recognition, verification, sound, words.
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Figure 1. Microphone |
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A simple and effective source code for Speaker Recognition. This code is based on Amin Koohi’s excellent submission available here and improves results using an advanced metric for distance computation. In this way a better recognition rate is achieved. On the initial dataset (8 speakers) we obtain a recognition rate of 100% (the previuos one was 87.5%). We can achieve analogous results (100% recognition rate) for a larger dataset (11 speakers). |
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Demo code (protected P-files) available for performance evaluation. Matlab Signal Processing Toolbox is required.
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Release
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Major features
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1.0
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2005.12.07 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Speaker Recognition System – Release 1.0 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 26 EUROS (less than 36,4 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Speaker Recognition System. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab Signal Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
Writing Fast Matlab Code #5:Vectorization
A computation is vectorized by taking advantage of vector operations. A variety of programming situations can be vectorized, and often improving speed to 10 times faster or even better. Vectorization is one of the most general and effective techniques for writing fast M-code.
Vectorized Computations
Many standard Matlab functions are “vectorized”: they can operate on an array as if the function had been applied individually to every element.
>> sqrt([1,4;9,16]) >> abs([0,1,2,−5,−6,−7]) ans = 1 2 3 4 ans = 0 1 2 5 6 7
Consider the following function:
function d = minDistance(x,y,z) % Find the min distance between a set of points and the origin nPoints = length(x); d = zeros(nPoints,1); % Preallocate for k = 1:nPoints % Compute distance for every point d(k) = sqrt(x(k)ˆ2 + y(k)ˆ2 + z(k)ˆ2); end d = min(d); % Get the minimum distance
For every point, its distance from the origin is computed and stored in d. For speed, array d is preallocated (see Section 3). The minimum distance is then found with min. To vectorize the distance
computation, replace the for loop with vector operations:
function d = minDistance(x,y,z) % Find the min distance between a set of points and the origin d = sqrt(x.ˆ2 + y.ˆ2 + z.ˆ2); % Compute distance for every point d = min(d); % Get the minimum distance
The modified code performs the distance computation with vector operations. The x, y and z arrays are first squared using the per-element power operator, .^ (the per-element operators for multiplication and division are .* and ./). The squared components are added with vector addition. Finally, the square root of the vector sum is computed per element, yielding an array of distances. (A further improvement: it is equivalent to compute d = sqrt(min(x.^2 + y.^2 + z.^2))).
The first version of the minDistance program takes 0.73 seconds on 50000 points. The vectorized version takes less than 0.04 seconds, more than 18 times faster.
Some useful functions for vectorizing computations:
min, max, repmat, meshgrid, sum, cumsum, diff, prod, cumprod, accumarray
Vectorized Logic
The previous section shows how to vectorize pure computation. But bottleneck code often involves conditional logic. Like computations, Matlab’s logic operators are vectorized:
<pre lang="matlab" escaped="true">>> [1,5,3] < [2,2,4] ans = 1 0 1
Two arrays are compared per-element. Logic operations return “logical” arrays with binary values. How is this useful? Matlab has a few powerful functions for operating on logical arrays:
>> find([1,5,3] < [2,2,4]) % Find indices of nonzero elements ans = 1 3 >> any([1,5,3] < [2,2,4]) % True if any element of a vector is nonzero % (or per−column for a matrix) ans = 1 >> all([1,5,3] < [2,2,4]) % True if all elements of a vector are nonzero % (or per−column for a matrix) ans = 0
Vectorized logic also works on arrays.
>> find(eye(3) == 1) % Find which elements == 1 in the 3x3 identity matrix ans = 1 % (element locations given as indices) 5 9
By default, find returns element locations as indices (see page 16 for indices vs. subscripts).
Example 1: Vector Normalization
To normalize a single vector v to unit length, one can use v = v/norm(v). However, to use norm for set of vectors v(:,1), v(:,2), . . . requires computing v(:,k)/norm(v(:,k)) in a loop. Alternatively,
vMag = sqrt(sum(v.ˆ2)); v = v./vMag(ones(1,size(v,1)),:);
Example 2: Removing elements
The situation often arises where array elements must be removed on some per-element condition. For example, this code removes all NaN and infinite elements from an array x:
i = find(isnan(x) | isinf(x)); % Find bad elements in x x(i) = []; % and delete them
Alternatively,
i = find(˜isnan(x) & ˜isinf(x)); % Find elements that are not NaN and not infinite x = x(i); % Keep those elements
Both of these solutions can be further streamlined by using logical indexing:
x(isnan(x) | isinf(x)) = []; % Delete bad elements
or
x = x(˜isnan(x) & ˜isinf(x)); % Keep good elements
Example 3: Piecewise functions
The sinc function has a piecewise definition,
sinc(x) = ( sin(x)/x, x = 0 1, x = 0
This code uses find with vectorized computation to handle the two cases separately:
function y = sinc(x) % Computes the sinc function per−element for a set of x values. y = ones(size(x)); % Set y to all ones, sinc(0) = 1 i = find(x ˜= 0); % Find nonzero x values y(i) = sin(x(i)) ./ x(i); % Compute sinc where x ˜= 0
A concise alternative is y = (sin(x) + (x == 0))./(x + (x == 0)).
Example 4: Drawing images with meshgrid
The meshgrid function takes two input vectors and converts them to matrices by replicating the first over the rows and the second over the columns.
>> [x,y] = meshgrid(1:5,1:3)
| x = |
y = |
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1 |
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5 | 2 |
2 |
2 |
2 |
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5 | 3 |
3 |
3 |
3 |
3 | ||
The matrices above work like a map for a width 5, height 3 image. For each pixel, the x-location can be read from x and the y-location from y. This may seem like a gratuitous use of memory as x and y simply record the column and row positions, but this is useful. For example, to draw an ellipse,
%Create x and y for a width 150, height 100 image [x,y] = meshgrid(1:150,1:100); % Ellipse with origin (60,50) of size 15 x 40 Img = sqrt(((x−60).ˆ2 / 15ˆ2) + ((y−50).ˆ2 / 40ˆ2)) > 1; % Plot the image imagesc(Img); colormap(copper); axis image, axis off
Drawing lines is just a change in the formula.
[x,y] = meshgrid(1:150,1:100); % The line y = x*0.8 + 20 Img = (abs((x*0.8 + 20) − y) > 1); imagesc(Img); colormap(copper); axis image, axis off
Polar functions can be drawn by first converting x and y variables with the cart2pol function.
[x,y] = meshgrid(1:150,1:100); <table border="0" cellspacing="0" cellpadding="0"> <tbody></tbody></table> [th,r] = cart2pol(x − 75,y − 50); % Convert to polar % Spiral centered at (75,50) Img = sin(r/3 + th); imagesc(Img); colormap(hot); axis image, axis off
Example 5: Polynomial interpolation
Given n points x1 , . . . xn and corresponding function values y1 , . . . yn , the coefficients c0 , . . . , cn−1 of the interpolating polynomial
can be found by solving:
function c = polyint(x,y) % Given a set of points and function values x and y, % computes the interpolating polynomial. x = x(:); % Make sure x and y are both column vectors y = y(:); n = length(x); % n = Number of points % Construct the matrix on the left−hand−side xMatrix = repmat(x, 1, n); % Make an n by n matrix with x on every column powMatrix = repmat(n−1:−1:0, n, 1); % Make another n by n matrix of exponents A = xMatrix .ˆ powMatrix; % Compute the powers c = A\y; % Solve matrix equation for coefficients
The strategy to construct the left-hand side matrix is to first make two n×n matrices of bases and exponents and then put them together using the per element power operator, .^ . The repmat function (“replicate matrix”) is used to make the base matrix xMatrix and the exponent matrix powMatrix.
The xMatrix is made by repeating the column vector x over the columns n times. The powMatrix is a row vector with elements n − 1, n − 2, n − 3, . . . , 0 repeated down the rows n times. The two matrices could also have been created with [powMatrix, xMatrix] = meshgrid(n-1:-1:0, x).
The code above is only an example—use the standard polyfit function for serious use.
Vectorization
A computation is vectorized by taking advantage of vector operations. A variety of programming situations can be vectorized, and often improving speed to 10 times faster or even better. Vectorization is one of the most general and effective techniques for writing fast M-code.
5.1 Vectorized Computations
Many standard Matlab functions are “vectorized”: they can operate on an array as if the function had been applied individually to every element.
>> sqrt([1,4;9,16])
>> abs([0,1,2,−5,−6,−7])
ans =
1 2
3 4
ans =
0 1 2 5 6 7
Consider the following function:
function d = minDistance(x,y,z)
% Find the min distance between a set of points and the origin
nPoints = length(x);
d = zeros(nPoints,1); % Preallocate
for k = 1:nPoints % Compute distance for every point d(k) = sqrt(x(k)ˆ2 + y(k)ˆ2 + z(k)ˆ2);
end
d = min(d); % Get the minimum distance
For every point, its distance from the origin is computed and stored in d. For speed, array d is preallocated (see Section 3). The minimum distance is then found with min. To vectorize the distance
computation, replace the for loop with vector operations:
function d = minDistance(x,y,z)
% Find the min distance between a set of points and the origin
d = sqrt(x.ˆ2 + y.ˆ2 + z.ˆ2); % Compute distance for every point d = min(d); % Get the minimum distance
The modified code performs the distance computation with vector operations. The x, y and z arrays are first squared using the per-element power operator, .^ (the per-element operators for multiplication and division are .* and ./). The squared components are added with vector addition. Finally, the square root of the vector sum is computed per element, yielding an array of distances. (A further improvement: it is equivalent to compute d = sqrt(min(x.^2 + y.^2 + z.^2))).
9
The first version of the minDistance program takes 0.73 seconds on 50000 points. The vectorized version takes less than 0.04 seconds, more than 18 times faster.
Some useful functions for vectorizing computations:
min, max, repmat, meshgrid, sum, cumsum, diff, prod, cumprod, accumarray
5.2 Vectorized Logic
The previous section shows how to vectorize pure computation. But bottleneck code often involves conditional logic. Like computations, Matlab’s logic operators are vectorized:
>> [1,5,3] < [2,2,4]
ans =
1 0 1
Two arrays are compared per-element. Logic operations return “logical” arrays with binary values. How is this useful? Matlab has a few powerful functions for operating on logical arrays:
>> find([1,5,3] < [2,2,4]) % Find indices of nonzero elements
ans =
1 3
>> any([1,5,3] < [2,2,4]) % True if any element of a vector is nonzero
% (or per−column for a matrix)
ans =
1
>> all([1,5,3] < [2,2,4]) % True if all elements of a vector are nonzero
% (or per−column for a matrix)
ans =
0
Vectorized logic also works on arrays.
>> find(eye(3) == 1) % Find which elements == 1 in the 3x3 identity matrix ans =
1 % (element locations given as indices)
5
9
By default, find returns element locations as indices (see page 16 for indices vs. subscripts).
10
Example 1: Vector Normalization
To normalize a single vector v to unit length, one can use v = v/norm(v). However, to use norm for set of vectors v(:,1), v(:,2), . . . requires computing v(:,k)/norm(v(:,k)) in a loop. Alternatively,
vMag = sqrt(sum(v.ˆ2));
v = v./vMag(ones(1,size(v,1)),:);
Example 2: Removing elements
The situation often arises where array elements must be removed on some per-element condition. For example, this code removes all NaN and infinite elements from an array x:
i = find(isnan(x) | isinf(x)); % Find bad elements in x x(i) = []; % and delete them
Alternatively,
i = find(˜isnan(x) & ˜isinf(x)); % Find elements that are not NaN and not infinite x = x(i); % Keep those elements
Both of these solutions can be further streamlined by using logical indexing:
x(isnan(x) | isinf(x)) = []; % Delete bad elements
or
x = x(˜isnan(x) & ˜isinf(x)); % Keep good elements
Example 3: Piecewise functions
The sinc function has a piecewise definition,
sinc(x) =
( sin(x)/x, x = 0
1, x = 0
This code uses find with vectorized computation to handle the two cases separately:
function y = sinc(x)
% Computes the sinc function per−element for a set of x values.
y = ones(size(x)); % Set y to all ones, sinc(0) = 1 i = find(x ˜= 0); % Find nonzero x values
y(i) = sin(x(i)) ./ x(i); % Compute sinc where x ˜= 0
A concise alternative is y = (sin(x) + (x == 0))./(x + (x == 0)).
11
Example 4: Drawing images with meshgrid
The meshgrid function takes two input vectors and converts them to matrices by replicating the first over the rows and the second over the columns.
>> [x,y] = meshgrid(1:5,1:3)
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x = |
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y = |
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3 |
The matrices above work like a map for a width 5, height 3 image. For each pixel, the x-location can be read from x and the y-location from y. This may seem like a gratuitous use of memory as x and y simply record the column and row positions, but this is useful. For example, to draw an ellipse,
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% Create x and y for a width 150, height 100 image
[x,y] = meshgrid(1:150,1:100);
% Ellipse with origin (60,50) of size 15 x 40
Img = sqrt(((x−60).ˆ2 / 15ˆ2) + ((y−50).ˆ2 / 40ˆ2)) > 1;
% Plot the image
imagesc(Img); colormap(copper);
axis image, axis off
Drawing lines is just a change in the formula.
[x,y] = meshgrid(1:150,1:100);
% The line y = x*0.8 + 20
|
Img = (abs((x*0.8 + 20) − y) > 1);
imagesc(Img); colormap(copper);
axis image, axis off
Polar functions can be drawn by first converting x and y variables with the cart2pol function.
[x,y] = meshgrid(1:150,1:100);
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[th,r] = cart2pol(x − 75,y − 50); % Convert to polar
% Spiral centered at (75,50) Img = sin(r/3 + th);
imagesc(Img); colormap(hot);
axis image, axis off
12
Example 5: Polynomial interpolation
Given n points x1 , . . . xn and corresponding function values y1 , . . . yn , the coefficients c0 , . . . , cn−1 of the interpolating polynomial
can be found by solving
P (x) = cn−1 x
n−1
+ · · · + c1 x + c0
|
|
x1 n−1 x1 n−2 · · · x1 2 x1 1 cn 1 1
n−1 x n−2 · · · x 2 x
1 c
y
x2 2
2 2
n−2
2
. .
. = .
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. .
.
xn n−1 xn n−2 · · · xn 2 xn 1 c0 yn
function c = polyint(x,y)
% Given a set of points and function values x and y,
% computes the interpolating polynomial.
x = x(:); % Make sure x and y are both column vectors y = y(:);
n = length(x); % n = Number of points
% Construct the matrix on the left−hand−side
xMatrix = repmat(x, 1, n); % Make an n by n matrix with x on every column powMatrix = repmat(n−1:−1:0, n, 1); % Make another n by n matrix of exponents
A = xMatrix .ˆ powMatrix; % Compute the powers
c = A\y; % Solve matrix equation for coefficients
The strategy to construct the left-hand side matrix is to first make two n×n matrices of bases and exponents and then put them together using the per element power operator, .^ . The repmat function (“replicate matrix”) is used to make the base matrix xMatrix and the exponent matrix powMatrix.
x(1) x(1) · · · x(1)
x(2) x(2) · · · x(2)
n − 1 n − 2 · · · 0
n − 1 n − 2 · · · 0
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xMatrix =
.
powMatrix =
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. . .
x(n) x(n) · · · x(n)
n − 1 n − 2 · · · 0
The xMatrix is made by repeating the column vector x over the columns n times. The powMatrix is a row vector with elements n − 1, n − 2, n − 3, . . . , 0 repeated down the rows n times. The two matrices could also have been created with [powMatrix, xMatrix] = meshgrid(n-1:-1:0, x).
The code above is only an example—use the standard polyfit function for serious use.
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GUI Examples #4: Explore simple string manipulation
function [] = GUI_4() % Demonstrate how to make a multiline editbox. % Produces a GUI with an editbox on the left and a listbox on the right. % The user is invited to enter text into the editbox, either hitting return % at the end of each line or letting it wrap automatically. When the % button is pushed, each line of text from the editbox is placed as an % entry into the listbox. Notice the difference between how a wrapped line % is treated and a returned line is treated in the lisbox. % % % Author: Matt Fig % Date: 7/15/2009 S.fh = figure('units','pixels',... 'position',[450 450 400 200],... 'menubar','none',... 'name','Verify Password.',... 'resize','off',... 'numbertitle','off',... 'name','GUI_4'); S.ed = uicontrol('style','edit',... 'units','pix',... 'position',[10 60 190 120],... 'min',0,'max',2,... % This is the key to multiline edits. 'string',{'Enter text here'; 'then push the button.'},... 'fontweight','bold',... 'horizontalalign','center',... 'fontsize',11); S.ls = uicontrol('style','list',... 'units','pix',... 'position',[210 60 180 120],... 'backgroundcolor','w',... 'HorizontalAlign','left'); S.pb = uicontrol('style','push',... 'units','pix',... 'position',[10 10 380 40],... 'HorizontalAlign','left',... 'string','Transfer',... 'fontsize',14,'fontweight','bold',... 'callback',{@pb_call,S}); uicontrol(S.ed) % Give the editbox control. function [] = pb_call(varargin) % Callback for edit. S = varargin{3}; E = get(S.ed,'string'); % Get the editbox string Ec = cellstr(E); % Turn the character array into a cell array. set(S.ls,'string',Ec)
Download full list of examples
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