1. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 1 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Speaker Identification Using a Pitch Detection Algorithm Presenters: Estefany Carrillo Roberto M. Meléndez Komal Syed Montgomery College Speech Processing Center Faculty Advisor: Dr. Uchechukwu Abanulo

2. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 2 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Presentation Outline Presenters: Estefany Carrillo Roberto M. Meléndez Komal Syed Montgomery College Speech Processing Center Faculty Advisor: Dr. Uchechukwu Abanulo

3. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 3 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Objectives To estimate the pitch contour of a given speech signal using autocorrelation To determine the effectiveness of pitch for speaker identification Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

4. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 4 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Speech Signals To understand pitch, one must first understand some basic concepts of speech signals To understand pitch, one must first understand some basic concepts of speech signals Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

5. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 5 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Voiced vs. Unvoiced Speech 5 Voiced  Quasi-periodic excitation  Modulation by vocal tract  Production of mainly vowels  High Energy Unvoiced  No periodic vibration of vocal chords  Noise-like nature  Production of most consonants  Low Energy Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

6. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 6 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Speech Signals Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

7. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 7 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Pitch Illustration Pitch period is the distance in time from one peak to the next Approximately the same for the same phoneme by the same speaker No periodicity, no frequency Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

8. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 8 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 How do we measure the pitch period Automatically? Correlation Measure of similarity between two signals Two signals compared by Sliding one signal by a certain time lag Multiplying both the overlapping regions Repeating the process and adding the products until there is no more overlap Cross-correlation – two different signals compared Autocorrelation – the same signal correlated Results in a maximum peak at which we set time = 0, and the rest of the correlation signals tapers of to zero Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

9. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 9 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Rationale for Autocorrelation Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary 1.A periodic (or quasi-periodic) signal will be similar from one period to the next 2.It is expected that the maximum peak in the autocorrelation function will occur at the pitch period value for each speech frame.

10. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 10 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Speech Classification Algorithm

11. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 11 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Speech Classification 1.Given a normalized speech signal (amplitudes from -1 to 1) 2.Since speech is non-stationary (changes characteristics frequently with time), we first segment this signal into short frames (of about 10 ms) 3.We then compute the average energy of each frame: 4.Based on a pre-determined threshold, we classify the speech into voiced or unvoiced or background Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

12. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 12 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Pitch Detection Algorithm

13. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 13 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Autocorrelation Based PDA 1.First we automatically assign a pitch of zero to every unvoiced or silence frame determined from the speech classification algorithm 2.We then compute the autocorrelation function of each voiced frame 3.A peak is searched for within the 2ms to 16ms range 4.The lag of this peak is considered the pitch period for that frame, and the pitch is computed as the inverse of that lag. Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Pitch = 0 Zero lag

14. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 14 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Autocorrelation Based PDA - Illustration

15. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 15 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Application and Results

16. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 16 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Speaker Recognition Reference Speech Feature Extraction Model Building Test Speech FeatureExtraction ComparisonRecognitionDecision SystemOutput

17. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 17 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Speaker Identification using PDA Reference Speech Pitch Detection Average Pitch of Signal Test Speech Pitch Detection and Detection and average averagepitchcomputationDistanceComputationSpeaker = Minimum = Minimumdistance SystemOutput Test Speech

18. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 18 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Experiment Group II: 10 Men Group I: 10 Women 1.Record each group member twice saying the same phrase 2.Record each group member saying a different phrase

19. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 19 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Categories Case I: Female/Same Phrase Case II: Male/Same Phrase Case III: Female/Different Phrase Case IV: Male Different Phrase Case V: Female and Male/Same Phrase Case VI: Female and Male/Different Phrase

20. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 20 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Procedure 1.Select a range of thresholds for unvoiced segments of speech Range = [0.001:0.0005:0.01] 2.Construct the pitch contour for each of the reference and test speech files for all thresholds 3.Using minimum distance criterion, determine the test speaker that matches the reference speaker

21. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 21 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Pitch Contours PITCHPITCH AMPLITUDEAMPLITUDE Reference Speaker Time (ms)

22. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 22 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 PITCHPITCH Matched Test Speaker AMPLITUDEAMPLITUDE Time (ms) Pitch Contours

23. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 23 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Best Threshold 3.Select threshold that gives maximum number of correctly matched speakers for each category

24. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 24 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Noise 4.Add different levels of noise (5dB to 30dB) to: Both reference and test speech filesBoth reference and test speech files Only reference speech fileOnly reference speech file Only test speech filesOnly test speech files 5.Examine the number of matched speakers vs. the level of SNR (Signal to Noise Ratio)

25. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 25 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Female/Same Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

26. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 26 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Male/Same Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

27. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 27 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Female/Different Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

28. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 28 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Male/Different Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

29. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 29 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Male and Female/Same Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

30. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 30 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Male and Female/Different Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

31. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 31 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

32. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 32 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary 1.Pitch detection algorithms are heavily dependent on speech segmentation accuracy 2.Pitch is somewhat effective as a simple speaker identifier Summary

33. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 33 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Results 3. As signal to noise ratios increase, the number of correctly identified speakers increases 4. There seems to be an optimum signal to noise ratio that gives the maximum number of correctly matched speakers

34. Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, 2008 34 Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet 2009 05/08/09 Presenters: Estefany Carrillo Roberto M. Meléndez Komal Syed Montgomery College Speech Processing Center Faculty Advisor: Dr. Uchechukwu Abanulo