Speaker Identification Using
Wavelet Analysis and ANN
DR. ANUPAM SHUKLA
DR. RITU TIWARI
HEMANT KUMAR MEENA
RAHUL KALA
Shukla, Anupam; Tiwari, Ritu; Meena, Hemant Kumar & Kala, Rahul; “Speaker Identification
using Wavelet Analysis and Artificial Neural Networks”, proceedings of the National
Symposium on Acoustics (NSA) 2008
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Index
1 . INTRODUCTION
2 . TECHNIQUES USED
3 . PROCEDURE
4 . RESULTS
5 . CONCLUSION
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Introduction
 Identification of a person is a very traditional problem.
 Finger
print recognition, face recognition, signature
recognition are common techniques.
 Speaker recognition or Automatic Speaker Identification
(ASI) identifies an author based on the words spoken.
 We have used wavelet analysis to extract the various
features and Artificial Neural Networks to identify the
speaker by the extracted features.
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Common Techniques
1.
Analysis techniques
Fourier Analysis
Short Time Fourier Analysis
Wavelet Analysis
2. Artificial Neural Networks
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Analysis Techniques
 We
have used Wavelet transform to extract
characteristics, which is an advancement over
Fourier analysis and Short Time Fourier Analysis
(STFT).
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Fourier Analysis
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Short Time Fourier Analysis
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Wavelet Analysis
 It is a windowing technique with variable-sized
regions.
 Wavelet analysis allows the use of different time
intervals for different type frequency information.
Indian Institute of Information Technology and Management Gwalior
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Wavelet Analysis(Cont..)
Indian Institute of Information Technology and Management Gwalior
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Wavelet Analysis(Cont..)
Capable of revealing aspects
of data
Wavelet packet method
Signal decomposition
Indian Institute of Information Technology and Management Gwalior
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Wavelet Packet Analysis
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Artificial Neural Network
 Excellent means of machine learning
 Reputed training of the system to learn the given
data
 Testing
 Performance
Indian Institute of Information Technology and Management Gwalior
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Procedure
 Collection of data sets
 Analysis of data sets (feature extraction)
 Training of ANN
 Testing
 Result
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Normalization of Data
Ii=(Vi - Mean(Vij) ) / (Max(Vij) - Mean(Vij) ), for all j
Here
Ii is th ith input of the neural network
Vi is the ith feature extracted from Wavelet Analysis
Mean(Vi) is the mean of all Vij found in the training
data set
Max(Vi) is the maximum of all Vij found in training
data set for all j in data set
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Feature Extracted
Indian Institute of Information Technology and Management Gwalior
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Result
 Performance of 97.5%
 This clearly shows that the algorithm works well and
gives correct results on almost all inputs.
 20 speakers and 40 test cases (39 correctly
identified)
Indian Institute of Information Technology and Management Gwalior
24/12/2008
Conclusions
Indian Institute of Information Technology and Management Gwalior
24/12/2008