1. Project 1 : Eigen-Faces Applied to Speech Style Classification Brad Keserich, Senior, Computer Engineering College of Engineering and Applied Science; University of Cincinnati; Cincinnati, Ohio Suryadip Chakraborty, School of Computing Sciences and Informatics Dr. Dharma Agrawal, Professor, School of Computing Sciences and Informatics 1 Sponsored ByThe National Science Foundation Grant ID No.: DUE-0756921

2. Introduction Speech recognition Voice disorders –Stuttering –Pausing –Other less known forms Research group focus on Parkinson’s Patients 2

3. Techniques Previous work –Good results using Neural Network classifiers using Fuzzy values –Wavelet Transformations are effective For this project –Eigen-faces method adapted to audio 3

4. Goals Investigate the usefulness of the eigen-faces method for speech classification 4

5. Objectives Acquire data Extract salient features Analyze Eigen-faces effectiveness 5

6. Eigen-faces for audio 6 w1w2 w3w4w5 t wivi = f1 f2 : : : : : fr

7. Classifiers using Abstract Features Training –Training set of feature vectors –Convert to Zero-mean truth set –Top k principle components (using principle component analysis (PCA)) Classifying –Project new vectors onto eigenbasis –Residuals indicate closeness to a class 7

8. Data Recorded word: “Ta-Be-Mo-No” –Consonant + vowel sounds –Easy to do segmentation –Use “Ta” portion only Use voice acting for data collection –Same person –Vary the way the word is spoken Variance of speaking style –Stuttering –Pausing –Pace –Pitch inflections 8

9. 9 Pipeline

10. Segmentation and Labeling Automation –Works well for slow clear cases –Not as well for more realistic cases –Slow cases are close to hand segmentation By Hand –More reliable segmentation at this point –Done with sample counts in Logic 8 –Label the segments with correct sound 10

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13. Modifications Use additional features in the Eigen- faces method –Stutter detection –Pauses and spacing within the spoken word –Pitch inflections Utilize Mel-Cepstrum to pick up features Substitute Laplacian Eigenmap for PCA 13

14. Results Features performing well –Blatant stutter detection –Long durations –Spectrum analysis Good class seperability 14

15. Conclusions Eigen-faces work for spoken audio data More tweaking required Further research –Mel-Cepstrum features –Laplacian Eigenmapping to replace PCA May be useful as a front end to Fuzzy- Neuro classifiers 15

16. References 1.Wu, H., Siegel, M., & Khosla, P. (1999). Vehicle sound signature recognition by frequency vector principal component analysis. IEEE Transactions on Instrumentation and Measurement, 48(5) doi: http://dx.doi.org/10.1109/19.799662. http://dx.doi.org/10.1109/19.799662 2.Belkin, M. & Niyogi, P. (2002). Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. 1.Prahalld, K. Speech Technology: A Practical Introduction Topics: Spectogram, Cepstrum and Mel-Frequency Analysis. http://www.speech.cs.cmu.edu/11- 492/slides/03_mfcc.pdf. 16