1. HIWIRE MEETING Torino, March 9-10, 2006 José C. Segura, Javier Ramírez

2. 2 HIWIRE Meeting – Torino, 9 -10 March, 2006 Schedule  HIWIRE database evaluations  New results: HEQ and PEQ  Non-linear feature normalization  Using temporal redundancy  HEQ integration in Loquendo platform  Recursive estimation of the equalization function  New improvements in robust VAD  Bispectrum-based VAD  SVM-enabled VAD

3. 3 HIWIRE Meeting – Torino, 9 -10 March, 2006 HIWIRE database evaluations

4. 4 HIWIRE Meeting – Torino, 9 -10 March, 2006 Schedule  HIWIRE database evaluations  New results: HEQ and PEQ  Non-linear feature normalization  Using temporal redundancy  HEQ integration in Loquendo platform  Recursive estimation of the equalization function  New improvements in robust VAD  Bispectrum-based VAD  SVM-enabled VAD

5. 5 HIWIRE Meeting – Torino, 9 -10 March, 2006 Temporal redundancy in HEQ  Enhance the normalization adding a linear transformation to restore temporal correlations  Each equalized cepstral coefficient is time-filtered with an ARMA filter that restores the autocorrelation of clean data AURORA4 AURORA2 (clean test)

6. 6 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration in Loquendo platform SEGMENTAL Actually implemented HIGH MISMATCH SENTENCE-BY-SENTENCE RECURSIVE New proposal

7. 7 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (1)  Actual approach: Gaussian HEQ using ECDF  Using quantiles

8. 8 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (2)  Equalization by linear interpolation Averaged over training data From actual utterance  Mapping corresponding quantiles

9. 9 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (3)

10. 10 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (4)  Utterances are equalized WITHOUT delay  Quantiles are updated AFTER the equalization

11. HIWIRE MEETING Torino, March 9-10, 2006 José C. Segura, Javier Ramírez

12. 12 HIWIRE Meeting – Torino, 9 -10 March, 2006 Schedule  HIWIRE database evaluations  New results: HEQ and PEQ  Non-linear feature normalization  Using temporal redundancy  HEQ integration in Loquendo platform  Recursive estimation of the equalization function  New improvements in robust VAD  Bispectrum-based VAD  SVM-enabled VAD

13. 13 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (1)  Motivations:  Ability of HOS methods to detect signals in noise  Knowledge of the input processes (Gaussian)  Issues to be addressed:  Computationally expensive  Variance of bispectrum estimators much higher than that of power spectral estimators (identical data record size)  Solution: Integrated bispectrum  J. K. Tugnait, “Detection of non-Gaussian signals using integrated polyspectrum,” IEEE Trans. on Signal Processing, vol. 42, no. 11, pp. 3137–3149, 1994.

14. 14 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (2)  Definitions: Let x(t) be a discrete-time signal  Bispectrum:  Third order cumulants:  Inverse transform:

15. 15 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (3) Noise onlyNoise + speech

16. 16 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (4)  Integrated bispectrum (IBI):  Cross-spectrum S yx (  )  Bispectrum Inverse transform:  Bispectrum – Cross spectrum: i= 0

17. 17 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (5)  Integrated bispectrum (IBI):  Defined as a cross spectrum between the signal and its square, and therefore, it is a function of a single frequency variable  Benefits:  Less computational cost computed as a cross spectrum  Variance of the same order of the power spectrum estimator  Properties  For Gaussian processes: Bispectrum is zero Integrated bispectrum is zero as well

18. 18 HIWIRE Meeting – Torino, 9 -10 March, 2006  Two alternatives explored for formulating the decision rule:  Estimation by block averaging (BA):  MO-LRT:  Given a set of N= 2m+1 consecutive observations: Bispectrum-based VAD (6)

19. 19 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (7)  LRT evaluation  IBI Gaussian Model  Variances  Defined in terms of  S ss (clean speech power spectrum)  S nn (noise power spectrum)

20. 20 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (8)  Denoising: Smoothed spectral subtraction 1 st WF design 1 st WF stage 2 nd WF design 2 nd WF stage 1-frame delay

21. 21 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum VAD Analysis (1)  MO-LRT VAD

22. 22 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD results (2)

23. 23 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD results (3)

24. 24 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD results (4) WF: Wiener filtering FD : Frame-dropping

25. 25 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (1)  Motivation:  Ability of SVMs for learning from experimental data  SVMs enable defining a function: using training data:  Classify unseen examples (x, y)  Statistical learning theory restricts the class of functions the learning machine can implement.

26. 26 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (2)  Hyperplane classifiers:  Training: w and b define maximal margin hyperplane  Kernels:

27. 27 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (3)

28. 28 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (4)  Feature extraction:  Training:

29. 29 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (5)  Feature extraction:  Decision function  2-band features

30. 30 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (6)  Analysis:  4 subbands  Noise reduction  Improvements:  Contextual speech features  Better results without noise reduction

31. 31 HIWIRE Meeting – Torino, 9 -10 March, 2006 Dissemination (VAD)  Integrated bispectrum:  J.M. Górriz, J. Ramírez, C. G. Puntonet, J.C. Segura, “Generalized-LRT based voice activity detector”, IEEE Signal Processing Letters, 2006.  J. Ramírez, J.M. Górriz, J. C. Segura, C. G. Puntonet, A. Rubio, “Speech/Non- speech Discrimination based on Contextual Information Integrated Bispectrum LRT”, IEEE Signal Processing Letters, 2006.  J.M. Górriz, J. Ramírez, J. C. Segura, C. G. Puntonet, L. García, “Effective Speech/Pause Discrimination Using an Integrated Bispectrum Likelihood Ratio Test”, ICASSP 2006.  SVM VAD:  J. Ramírez, P. Yélamos, J.M. Górriz, J.C. Segura. “SVM-based Speech Endpoint Detection Using Contextual Speech Features”, IEE Electronics Letters 2006.  J. Ramírez, P. Yélamos, J.M. Górriz, C.G. Puntonet, J.C. Segura. “SVM- enabled Voice Activity Detection”, ISNN 2006.  P. Yélamos, J. Ramírez, J.M. Górriz, C.G. Puntonet, J.C. Segura, “Speech Event Detection Using Support Vector Machines”, ICCS 2006.

32. HIWIRE MEETING Athens, November 3-4, 2005 José C. Segura, Javier Ramírez