Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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.

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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

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

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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

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

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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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)

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

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

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

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

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

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

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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

Look Who’s Talking Now SEM Exchange, Fall 2008 October 9, Montgomery College Speaker Identification Using Pitch Engineering Expo Banquet /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

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