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Voiceprint System Development Design, implement, test unique voiceprint biometric system

Voiceprint System Development Design, implement, test unique voiceprint biometric system. Research Day Presentation, May 3 rd 2013 Rahul Raj ( Team Lead), Geeta Bothe, Mahesh Sooryambylu , Ravi Ray, Sreeram Vancheeswaran IBM India Customer: Jonathan Leet (DPS 2013)

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Voiceprint System Development Design, implement, test unique voiceprint biometric system

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  1. Voiceprint System DevelopmentDesign, implement, test unique voiceprint biometric system Research Day Presentation, May 3rd 2013 Rahul Raj (Team Lead), Geeta Bothe, Mahesh Sooryambylu, Ravi Ray, Sreeram Vancheeswaran IBM India Customer: Jonathan Leet (DPS 2013) Instructor: Dr. Charles Tappert

  2. Common Passphrase • Background: four possible types of passphrases • User-specified phrase, like the user's name • Specified phrase common to all users • “My name is” from phrase “My name is user’s name” • Random phrase displayed on the computer screen • Random phrase that can vary at the user's discretion • Advantages of a Common Passphrase • Simplifies the segmentation problem • Allows for careful selection of common phrase to optimize variety of phonetic units for their authentication value • Facilitates testing for imposters • Permits the measurement of true voice authentication biometric performance • Avoids potential experimental flaws

  3. Software Used: Audacity & Matlab • Audacity • Open source audio editing software supports sound recording and editing. • Supports resampling and stereo to mono conversion • Available all platforms: Windows, Linux, Mac • Matlab • Signal Processing Toolbox provides industry-standard algorithms and apps for analog and digital signal processing • Supports visualizing signals in time and frequency domains, FFT computation for spectral analysis, resampling, and other signal processing techniques.

  4. System Architecture Pace’s Biometric Authentication System will obtain performance results from the feature vectors

  5. Voice Sample  Spectrogram using Matlab Input speech Sample (Mono, 44100 Samples/sec) • Voice stream collected into 1024 frames • Samples are read sliding stream by 512 bytes, maintaining overlap • Represent samples of a frame • One Frame ~ 23ms since • Frame size = 44100/1024 • Length of one frame = 1000ms/frame size

  6. Voice Sample  Spectrogram using Matlab • Represent component frequencies of a frame after applying FFT • Frequency Vs Time data Represent the complete spectral data available for processing Spectrogram constructed out of the above values Voiceprint Systems CS692 2013 Spring Batch

  7. Mel-Frequency bands space filters appropriately • Corresponds to frequency transform performed by the cochlea of human ear. • Mel filters are shown below, 13 lower bands are used for processing.

  8. Segmenting “My Name Is” • Speech Waveform indicating the voiced and unvoiced segments • Energy vs Zero Crossing plotted for same speech sample • Non-voiced segments captures high zero crossing rate(red) and low energy(green) values • Voiced segments indicate low zero crossing rate and high energy values • Higher frequency components of ‘z’ sound will have higher energy compared to the other phonemes • Diagram shows the automatically Marked Spectrum in Matlab • Vertical lines demarcate speech beginning and end of ‘z’ Voiceprint Systems CS692 2013 Spring Batch

  9. Seven sound units of “My name is”

  10. Discrete Time Warp (TDW) AlgorithmSegments a Sample into Seven Sounds • DTW operates on spectrographic data: amp x freq x time • To segment a speech sample into the seven sound units, a sample’s time sequence is "warped" non-linearly against a manually sound segmented sample. Sample warp path represents the cost matrix and the warped path for the two time series represented long the axes The decision to find the next point in the warp W(i, j) is: If the warp path passes through D(i, j) then the sample Xi is warped to the point Yi. If there is a vertical section in the warp path, a single point in series X is warped to multiple points of series Y. Voiceprint Systems CS692 2013 Spring Batch

  11. Feature Extraction • Features measurements reduce data & characterize speaker • The features extracted: • Energy mean and variance in each frequency band over the entire utterance (~13*2 = 26 features) • Energy mean in each frequency band within each of the 7 phonetic sounds (~13*7 = 91 features) • Voice Fundamental Frequency (F0) – not completed • Voice Formant Frequencies (F1-F3) – not completed • Feature extractor output is a fixed-length vector appropriate as input to Pace University Biometric Authentication System Note: 13 is the number of frequency bands

  12. System Performance • Performance was measured on 20 sample utterances from each of 30 speakers, manually segmented into the seven sounds. • Receiver Operating Characteristic (ROC) curves were obtained to find the Equal Error Rate (EER) and system performance from two feature sets.

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