Speaker Identification Using a Pitch Detection Algorithm

1. Speaker Identification Using a Pitch Detection Algorithm Presenters: Estefany Carrillo Roberto M. Meléndez Komal Syed Montgomery College Speech Processing Center Faculty Advisor: Dr. UchechukwuAbanulo

2. Presentation Outline • Introduction • Speech Classification Algorithm • Pitch Detection Algorithm • Application and Results • Summary Presenters: Estefany Carrillo Roberto M. Meléndez Komal Syed Montgomery College Speech Processing Center Faculty Advisor: Dr. UchechukwuAbanulo

3. Objectives Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary • To estimate the pitch contour of a given speech signal using autocorrelation • To determine the effectiveness of pitch for speaker identification

4. 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. Voiced vs. Unvoiced Speech • 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 5

6. Speech Signals Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

7. Pitch Illustration Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary No periodicity, no frequency • Pitch period is the distance in time from one peak to the next • Approximately the same for the same phoneme by the same speaker

8. 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. Rationale for Autocorrelation A periodic (or quasi-periodic) signal will be similar from one period to the next It is expected that the maximum peak in the autocorrelation function will occur at the pitch period value for each speech frame. Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

10. Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Speech Classification Algorithm

11. Speech Classification Given a normalized speech signal (amplitudes from -1 to 1) Since speech is non-stationary (changes characteristics frequently with time), we first segment this signal into short frames (of about 10 ms) We then compute the average energy of each frame: 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. Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Pitch Detection Algorithm

13. Autocorrelation Based PDA First we automatically assign a pitch of zero to every unvoiced or silence frame determined from the speech classification algorithm We then compute the autocorrelation function of each voiced frame A peak is searched for within the 2ms to 16ms range 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 Pitch = 0 Zero lag

14. Autocorrelation Based PDA - Illustration Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

15. Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Application and Results

16. Reference Speech Feature Extraction Model Building Test Speech Feature Extraction Recognition Decision Comparison Speaker Recognition Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary System Output

17. Reference Speech Pitch Detection Average Pitch of Signal Test Speech Test Speech Test Speech Pitch Detection and average pitch computation Speaker = Minimum distance Distance Computation Speaker Identification using PDA Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary System Output

18. Experiment Group I: 10 Women Group II: 10 Men Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary • Record each group member twice saying the same phrase • Record each group member saying a different phrase

19. 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. Procedure Select a range of thresholds for unvoiced segments of speech Range = [0.001:0.0005:0.01] Construct the pitch contour for each of the reference and test speech files for all thresholds Using minimum distance criterion, determine the test speakerthat matches the reference speaker

21. Pitch Contours Reference Speaker AMP L I T U D E Time (ms) P I T C H Time (ms)

22. Pitch Contours Matched Test Speaker AMP L I T U D E Time (ms) P I T C H Time (ms)

23. Best Threshold • Select threshold that gives maximum number of correctly matched speakers for each category Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

24. Noise Add different levels of noise (5dB to 30dB) to: Both reference and test speech files Only reference speech file Only test speech files Examine the number of matched speakers vs. the level of SNR (Signal to Noise Ratio)

25. Female/Same Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

26. Male/Same Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

27. Female/Different Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

28. Male/Different Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

29. Male and Female/Same Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

30. Male and Female/Different Phrase Noise Added to Both Files Noise Added to Reference File Noise Added to Test File

31. Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary Summary

32. Summary Pitch detection algorithms are heavily dependent on speech segmentation accuracy Pitch is somewhat effective as a simple speaker identifier Introduction Speech Classification Algorithm Pitch Detection Algorithm Application and Results Summary

33. 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. Presenters: Estefany Carrillo Roberto M. Meléndez Komal Syed Montgomery College Speech Processing Center Faculty Advisor: Dr. UchechukwuAbanulo