1. Neuro-Fuzzy and Soft Computing for Speaker Recognition (語者辨識)
2018/12/7
1999 CSIST
Neuro-Fuzzy and Soft Computing for Speaker Recognition (語者辨識)
J.-S. Roger Jang (張智星)
CS Dept., Tsing Hua Univ., Taiwan
...
In this talk, we are going to apply two neural network controller design techniques to fuzzy controllers, and construct the so-called on-line adaptive neuro-fuzzy controllers for nonlinear control systems. We are going to use MATLAB, SIMULINK and Handle Graphics to demonstrate the concept. So you can also get a preview of some of the features of the Fuzzy Logic Toolbox, or FLT, version 2.

2. Outline Introduction Data acquisition Feature extraction
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Outline
Introduction
Data acquisition
Feature extraction
Data reduction
Condensing, editing, fuzzy clustering
Fuzzy classifier refinement
Random search
Experiments
Conclusions and future work
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3. Speaker Recognition Types: Methodologies involved:
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Speaker Recognition
Types:
Text-dependent or text-independent
Close-set or open-set
Methodologies involved:
Digital signal processing
Pattern recognition
Clustering or vector quantization
Nonlinear optimization
Neuro-fuzzy techniques
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
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4. Data Acquisition Recording: Samples Recording program of Windows 95/98
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Data Acquisition
Recording:
Recording program of Windows 95/98
8 kHz sampling rate, 8-bit resolution (worse than phone quality)
5-second speech signal takes about 40KB.
Samples
Speaker #1
Speaker #2
Speaker #3
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5. Feature Extraction Major steps: Optional steps:
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Feature Extraction
Major steps:
Overlapping frames of 256 points (32 ms)
Hamming windowing to lessen distortion
cepstrum(frame) = real(IFFT(log|FFT(frame)|))
FFT = Fast Fourier Transform
IFFT = Inverse FFT
A feature vector consists of the first 14 cepstral coefficients of a frame.
Optional steps:
Frequency-selective filter to reduce noise
Mel-warped cepstral coefficients
Feature-wise normalization
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6. Physical Meanings of Cepstrum
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Physical Meanings of Cepstrum
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7. Feature Extraction 2.39 sec. speech signal low-pass filter take frames
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Feature Extraction
2.39 sec.
speech signal
148 frames
of 256 points
low-pass filter
take frames
Hamming windowing
IFFT
resample
log
abs
FFT
real
first 14 coefficients
normalization
148 feature vectors
of length 14
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8. Feature Extraction Upper: speaker #1 , lower: speaker #2 2018/12/7

9. Pattern Recognition Schematic diagram: Design (off-line) Sample speech
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Pattern Recognition
Schematic diagram:
Design (off-line)
Feature
Extraction
Data
Reduction
Sample speech
Sample set
Application (on-line)
Classifier
Feature
Extraction
Recognized
speaker
Test speech
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10. Pattern Recognition Methods
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Pattern Recognition Methods
K-NNR: K nearest neighbor rule
Euclidean distance
Mahalanobis distance
Maximum log likelihood
Adaptive networks
Multilayer perceptrons
Radial basis function networks
Fuzzy classifiers with random search
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11. K-Nearest Neighbor Rule (K-NNR)
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K-Nearest Neighbor Rule (K-NNR)
Steps:
1. Find the first k nearest neighbors of a given point.
2. Determine the class of the given point by a voting mechanism among these k nearest neighbors.
: class-A point
: class-B point
: point with unknown class
Circle of 3-nearest neighbors
The point is class B via 3-NNR.
Feature 2
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Feature 1

12. Decision Boundary for 1-NNR
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Decision Boundary for 1-NNR
Voronoi diagram: piecewise linear boundary
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13. Distance Metrics Euclidean distance Mahalanobis distance 2018/12/7

14. Data Reduction Purpose: Techniques: Reduce NNR computation load
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Data Reduction
Purpose:
Reduce NNR computation load
Increase data consistency
Techniques:
To reduce data size:
Editing: To eliminate noisy (boundary) data
Condensing: To eliminate redundant (deeply embedded) data
Vector quantization: To find representative data
To reduce data dimensions:
Principal component projection: To reduce the dimensions of the feature sets
Discriminant projection: To find the best set of vectors which best separates the patterns
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15. 2018/12/7
Editing
To remove noisy (boundary) data
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16. Condensing To remove redundant (deeply embedded) data 2018/12/7

17. VQ: Fuzzy C-Means Clustering
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VQ: Fuzzy C-Means Clustering
A point can belong to various clusters with
various degrees.
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18. A fuzzy classifier is equivalent to a 1-NNR if all MFs have the
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Fuzzy Classifier
Rule base:
if x is close to (A1 or A2 or A3), then class =
if x is close to (B1 or B2 or B3), then class = A3
A fuzzy classifier is equivalent
to a 1-NNR if all MFs have the
same width. v A2 B1 v B2 A1 v
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19. Fuzzy Classifier Adaptive network representation: x1 + y - x2
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Fuzzy Classifier
Adaptive network representation: A1
max A2 x1 A3 + y S x2 B1 -
max B2 B2
multidimensional MFs
x = [x1 x2] belongs to class if y > 0
class if y < 0
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20. Refining Fuzzy Classifier
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Refining Fuzzy Classifier
MFs with
the same width v v v
MFs’ widths
refined via
random search
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21. Principal Component Projection
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Principal Component Projection
Eigenvalues of covariance matrix: l1 > l2 > l3 > ... > ld
Projection on v1 & v Projection on v3 & v4
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22. Discriminant Projection
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Discriminant Projection
Best discriminant vectors : v1, v2, ... , vd
Projection on v1 & v Projection on v3 & v4
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23. Experiments Experimental data : Experiments :
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Experiments
Experimental data :
Sample size = 578, test size = 1063, no. of class = 3
No. of each speaker for sample data :
No. of each speaker for test data :
Experiments :
K-NNR with all sample data
K-NNR with reduced sample data
Fuzzy classifier refined via random search
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24. Performance Using All Samples
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Performance Using All Samples
Sample size = 578
Test size = 1063
Recognition rates as functions of
the speech signal length
Confusion
matrix
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25. 2018/12/7
Performance After E & D
Sample size = 497 after editing, 64 after condensing
Test size = 1063
Recognition rates as functions of
the speech signal length
Confusion
matrix
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26. Performance After VQ (FCM)
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Performance After VQ (FCM)
Sample size = 60 after FCM
Test size = 1063
Recognition rates as functions of
the speech signal length
Confusion
matrix
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27. Performance After VQ + RS
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Performance After VQ + RS
Sample (rule) size = 60, tuned via random search
Test size = 1063
Recognition rates as functions of
the speech signal length
Confusion
matrix
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28. On-line Recognition & Hardware Setup
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On-line Recognition & Hardware Setup
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29. Conclusions Performance after editing and condensing is unpredictable.
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Conclusions
Performance after editing and condensing is unpredictable.
Performance after VQ (FCM) is consistently better than that of editing and condensing.
A simple derivative-free optimization method, I.e., random search, can significantly enhance the performance.
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30. Future work Data dimension reduction
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Future work
Data dimension reduction
Other feature extraction methods (e.g., LPC)
Scale up the problem size
More speakers (ten or more)
Other vocal signals (laughter, coughs, singing, etc.)
Other biometric identification using
Faces
Fingerprints and palm prints
Retina and iris scans
Hand shapes/sizes/proportions
Hand vein distributions
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