1. Indian Institute of Information Technology and Management Gwalior24/12/2008 DR. ANUPAM SHUKLA DR. RITU TIWARI HEMANT KUMAR MEENA RAHUL KALA Speaker Identification Using Wavelet Analysis and ANN 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

2. Indian Institute of Information Technology and Management Gwalior24/12/2008 1. INTRODUCTION 2. TECHNIQUES USED 3. PROCEDURE 4. RESULTS 5. CONCLUSION Index

3. Indian Institute of Information Technology and Management Gwalior24/12/2008 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.

4. Indian Institute of Information Technology and Management Gwalior24/12/2008 Common Techniques 1. Analysis techniques Fourier Analysis Short Time Fourier Analysis Wavelet Analysis 2. Artificial Neural Networks

5. Indian Institute of Information Technology and Management Gwalior24/12/2008 Analysis Techniques We have used Wavelet transform to extract characteristics, which is an advancement over Fourier analysis and Short Time Fourier Analysis (STFT).

6. Indian Institute of Information Technology and Management Gwalior24/12/2008 Fourier Analysis

7. Indian Institute of Information Technology and Management Gwalior24/12/2008 Short Time Fourier Analysis

8. Indian Institute of Information Technology and Management Gwalior24/12/2008 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.

9. Indian Institute of Information Technology and Management Gwalior24/12/2008 Wavelet Analysis(Cont..)

10. Indian Institute of Information Technology and Management Gwalior24/12/2008 Wavelet Analysis(Cont..) Capable of revealing aspects of data Wavelet packet method Signal decomposition

11. Indian Institute of Information Technology and Management Gwalior24/12/2008 Wavelet Packet Analysis

12. Indian Institute of Information Technology and Management Gwalior24/12/2008 Artificial Neural Network Excellent means of machine learning Reputed training of the system to learn the given data Testing Performance

13. Indian Institute of Information Technology and Management Gwalior24/12/2008 Procedure Collection of data sets Analysis of data sets (feature extraction) Training of ANN Testing Result

14. Indian Institute of Information Technology and Management Gwalior24/12/2008 Normalization of Data I i =(V i - Mean(V ij ) ) / (Max(V ij ) - Mean(V ij ) ), for all j Here I i is th i th input of the neural network V i is the ith feature extracted from Wavelet Analysis Mean(V i ) is the mean of all V ij found in the training data set Max(V i ) is the maximum of all V ij found in training data set for all j in data set

15. Indian Institute of Information Technology and Management Gwalior24/12/2008 Feature Extracted

16. Indian Institute of Information Technology and Management Gwalior24/12/2008 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)

17. Indian Institute of Information Technology and Management Gwalior24/12/2008 Conclusions