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Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 1. Abhyudai Yadav Arnika Kumar Dvizma Sinha Jhalak R. Sharma Mentors: Dr. Mandeep Goyal & Dr. Kumar Vishal Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Final Review 7th May, 2015
- 2. ● Rationale & Objectives ● Methodology ● Literature Review ● Proposed Solution ● MatLab Simulations : Graphs ● Observations & Results ● Difficulties ● Future Work ● References Contents Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 3. Rationale Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Speaker Recognition Process of automatically recognizing who is speaking on the basis of an individual’s discrete information included in speech waves. Problem Presence of Reverberation in input speech signal Reduces recognition rate for speech recognition engines. TTo counter this problem Reverberation Mitigation is required.
- 4. Methodology Problem Analysis Feature Extraction Training Testing Performance Analysis Mitigation Performance Improvisation Mid Term I Review Mid Term II Review Final Review Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 5. Objectives Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation 1. • To mitigate reverberation from a speech signal. 2. • To improve the performance of automatic speaker recognition system in reverberant environments.
- 6. • Early reflections don’t distort the speech too much. • Late reflections main cause of distortion. Proposed Solution Idea : To identify & remove the regions of the signal with most reverberant energy. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Figure: Reverberation [Source: hyperphysics.phy]
- 7. • Envelope of late reflections -> described by an exponential decay curve. • T60 found to get slope of decay curve. • Regions identified in reverberant speech signal having same slope. • Reverberant regions removed using a binary mask. Proposed Solution [Matlab Simulation] 𝑒 𝑟 𝑛 ≈ exp −1 𝜏 𝑛 = 𝑎 𝑛
- 8. ● Reverberation time characterizes quality of auditory space T60 Parameter Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Figure: A) Speech Signal B) Reverberation tail C) Decay Rate D) Linear fit [Source: Rama Ratnam; Blind estimation of Reverberation Time; University Of Illinois] ● T30 and T20 are other such parameters ● Higher value of T60 signifies more reverberation and vice versa The T60 parameter measures the time taken for the sound level to drop 60 dB below the level at sound cessation
- 9. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Algorithm Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n) Algorithm run on each band individually By passing through Butterworth filter Of each point on the envelope 0 for exponential 1 otherwise Using Hilbert Transform
- 10. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n)
- 11. Time Domain Plots [A] [B] Time Domain Representations of: [A] Clean Speech [B] Reverberated Signal Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation [Matlab Simulation]
- 12. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n)
- 13. [Matlab Simulation] Butterworth Filter Bank (Log Scale) Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 14. Butterworth Filter Bank (Log Scale, 3 bins/octave) [Matlab Simulation] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 15. Channels obtained from filtering [Matlab Simulation] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 16. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n)
- 17. Envelope of the channels [Matlab Simulation] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 18. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n)
- 19. Estimating T60 [Matlab Simulation: Convergence of T60] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 20. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n)
- 21. Mask for the channels [Matlab Simulation] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 22. Mask for one channel [Matlab Simulation] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 23. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation Reconstruct signal Smooth mask Convert to log- scale (dB) Envelope Follower Filter bank Calculate binary mask Estimate true slope Find local slopes yr(n) yc(n)
- 24. Process of Dereverberation (in Time Domain)
- 25. Time Domain Plots [A] [B] Time Domain Representations of: [A] Clean Speech [B] Reverberated Signal [C] De-reverberated Signal Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation [Matlab Simulation] [C]
- 27. Spectrograms [Matlab Simulation] Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 28. Observations • Higher T60 value -> Better accuracy. • Percussive sounds easier to dereverberate • Speech signals difficult to dereverberate. As the envelopes situated far apart there is sufficient reverberant decay between them. Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation [Matlab Simulation]
- 29. Results Speech Recognition has two phases : Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 30. Difficulties Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation • Sampling rate of input: > 10000 Hz • Minimize effect of aliasing in the analog to digital conversion.
- 31. • Also known as signal processing front end. Future Work Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation
- 32. Thank You Performance Improvisation of Automatic Speaker Recognition by Spectral Reverberation Mitigation