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Feature selection
techniques for AER
Prepared by:-
Avinash Shukla(2022PCS2001)
Pratigya sharma(2022PCS2013)
Ajay kumar(2022PCS2019)
Piyush kumar Jaiswal(2022PCS2022)
Sudhanshu Gautam (2022PCS2029)
AER system setup
AER system divided into four sections:-
 Signal acquisition
 Feature extraction
 Feature selection
 classification
Feature extraction techniques used
 Melscaled power spectrum(MSPS)
 Mel frequency cepstral coefficients(MFCC)
 Shifted delta coefficients
Feature selection technique
 Lasso regression
 Ridge regression
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
Sample data taken from:-
 SAVEE database  eNTERFACE database
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
Accuracy for single feature selection:-
 Neutral emotion has shown better recognition while fear and surprise
have depicted low AER
AER from combination of two features
AER from combination of three features
Problem with combine features:-
 Redundancy issue
 Bias & variance problem
 Required more computation time
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
Feature selection using Lasso and ridge
regression
Feature selection ratio for Lasso and
ridge regression
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%
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%
THANK YOU !

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Feature selection techniques for AER.pptx

  • 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
  • 3.
  • 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%