Feature

1. Feature selection
techniques for AER
Prepared by:-
Avinash Shukla(2022PCS2001)
Pratigya sharma(2022PCS2013)
Ajay kumar(2022PCS2019)
Piyush kumar Jaiswal(2022PCS2022)
Sudhanshu Gautam (2022PCS2029)

2. AER system setup
AER system divided into four sections:-
 Signal acquisition
 Feature extraction
 Feature selection
 classification

4. Feature extraction techniques used
 Melscaled power spectrum(MSPS)
 Mel frequency cepstral coefficients(MFCC)
 Shifted delta coefficients

5. Feature selection technique
 Lasso regression
 Ridge regression

6. Classification
 Support vector machine(SVM) is used to classify emotion class
 SVM has utilized Linear and Radial Basis Function (rbf) kernel for generating
feature representative models based on training vectors

7. Sample data taken from:-
 SAVEE database  eNTERFACE database

8. Result and Analysis
AER using MSPS, MFCC, SDC and their combinations on SAVEE database
 AER from single features
 For inter-class value of confusion matrix should be high
 For intra-class value of confusion matrix should be less

9. Accuracy for single feature selection:-
 Neutral emotion has shown better recognition while fear and surprise
have depicted low AER

10. AER from combination of two features

11. AER from combination of three features

12. Problem with combine features:-
 Redundancy issue
 Bias & variance problem
 Required more computation time

13. Feature selection
 Use to overcome redundancy and computation complexity
 Lasso regression shrinks larger dimension to small one by setting some of
coefficient to zero
 Ridge regression is also feature shrinking algorithm like Lasso but it doesn’t
make coefficients zero
 Feature selection ratio(FSR) is less for ridge regression

14. Feature selection using Lasso and ridge
regression

15. Feature selection ratio for Lasso and
ridge regression

16. Performance comparison on eNTERFACE
and with SAVEE database
 The performance for different database is different
 For savee database accuracy 72.5%
 For eNTERFACE database accuracy is 56.41%

17. Conclusion
 AER in stressed speech
 Initially individually Accuracy is measured for MSPS,MFCC and SDC
 For better accuracy this features are combined
 This lead to increase in dimensions which required more computation time
 To overcome this issue Lasso and ridge regularization is used
 Accuracy for Savee database 72.5%
 Accuracy for eNTERFACE database 46.41%

18. THANK YOU !