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Structure-Based Speech Classification Using Nonlinear Embedding Techniques

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  1. Structure-Based Speech Classification Using Nonlinear Embedding Techniques Uchechukwu Ofoegbu Advisor Dr. Robert E. Yantorno Committee Dr. Saroj K. Biswas Dr. Henry M. Sendaula

  2. Acknowledgment • Dr. Robert Yantorno • Dr. Saroj Biswas • Dr. Henry Sendaula • Speech Lab Members • Air Force Research Laboratory, Rome, NY

  3. Overview • Voiced and Unvoiced Speech • Usable and Unusable Speech • Nonlinearities in Speech • Non-Linear Embedding • Research Goal • Proposed Research

  4. Voiced and Unvoiced Speech

  5. Voiced Quasi-periodic excitation Modulation by vocal tract Production of vowels, voiced fricatives & plosives Voiced/Unvoiced Characteristics • Unvoiced • No periodic vibration of vocal chords • Noise-like nature • Production of unvoiced fricatives and plosives

  6. Usable Speech • Portions of co-channel speech still usable for applications such as Speaker ID and Speech Recognition. • Low-energy (unvoiced/silence) segments overlap with high-energy (voiced) segments • Target-to-interferer Ratio (TIR) > 20dB

  7. Nonlinearities in Speech • Glottal waveform changes • Shape varies with amplitude • Physical observations • Flow in vocal tract is non-laminar • Coupling between vocal tract and folds • When glottis is open, prominent changes are observed in formant characteristics

  8. Nonlinear Embedding • Nonlinear Systems • Point moving along some trajectory in an abstract state space • Coordinates of the point are independent degrees of freedom of the system • State space could be reconstructed from a scalar signal

  9. Nonlinear Embedding (cont’d) • Takens’ Method of Delays • A state space representation topologically equivalent to the original state space of a system can be reconstructed from a single observable dimension • Vectors in m-dimensional state space are formed from time-delayed values of a signal

  10. Nonlinear Embedding (cont’d) • m = embedding dimension • d = delay value

  11. Nonlinear Embedding (Cont’d) • Delay value, d: • Dependent on sampling rate and signal properties • Large enough such that nonlinearities are taken into account by the reconstructed trajectory • Small enough to retain reasonable time resolution

  12. Nonlinear Embedding (Cont’d) • Dimension, m: • Generation of voiced speech constitutes a low-dimensional system • Generation of unvoiced speech constitutes a relatively high-dimensional system • Using a low dimension (such as m = 3) sufficiently reconstructs voiced but not unvoiced speech

  13. Embedded Voiced and Unvoiced Speech

  14. Embedded Usable and Unusable Speech

  15. Research Goal • Feature Extraction • Difference-Mean Comparison (DMC) Measure • Voiced/unvoiced classification • Nodal Density Measure • Voiced/unvoiced classification • Usable/unusable classification

  16. Difference-Mean Comparison (DMC) Measure Voiced/Unvoiced Classification

  17. Introduction • 3rd order difference computation along first non-singleton dimension • Ist order difference of NxN matrix given by • Length(3rd order diff. > mean) observed

  18. Embedded Voiced and Unvoiced Speech

  19. Difference-Mean Comparison Distribution

  20. Difference-Mean Comparison Distribution

  21. Difference-Mean Comparison Distribution

  22. DMC-Based Decisions

  23. DMC-Based Decisions

  24. DMC-Based Decisions

  25. DMC-Based Decisions

  26. DMC-Based Decisions

  27. DMC-Based Decisions

  28. Results

  29. Results (Cont’d)

  30. Nodal Density Measure Voiced/Unvoiced Classification Usable/Unusable Classification

  31. Introduction • Smallest cube which encloses the signal is determined • This cube is divided into N smaller cubes • Edges of the smaller cubes are defined as nodes • Number of nodes spanned by the signal is determined • Ratio of number of nodes spanned to total number of nodes is defined as nodal density

  32. Voiced/Unvoiced Classification

  33. Embedded Voiced and Unvoiced Speech Frames with Grids

  34. Nodes Spanned by Embedded Voiced and Unvoiced Speech Frames

  35. Nodal-Density Distribution

  36. Nodal-Density Distribution

  37. Nodal-Density Distribution

  38. Filtering • Moving Average Filter • Order, M = 10

  39. Nodal-Density Distributions after Filtering

  40. Nodal-Density Distributions after Filtering

  41. Nodal-Density Distributions After Filtering

  42. Results

  43. Results (Cont’d)

  44. Proposed Research Usable/Unusable Classification

  45. Embedded Usable and Unusable Speech Frames with Grids

  46. Nodes Spanned by Embedded Co-channel Speech of 30dB TIR Nodes Spanned by Embedded Co-channel Speech of 30dB TIR Nodes Spanned by Embedded Co-channel Speech of 30dB TIR 6000 6000 6000 4000 4000 4000 2000 2000 2000 0 0 0 -2000 -2000 -4000 -4000 -2000 -6000 -6000 -4000 5000 5000 5000 5000 5000 0 0 6000 0 0 4000 -5000 -5000 0 2000 -5000 -5000 0 -10000 -10000 -10000 -10000 -2000 -5000 -4000 Nodes Spanned by Embedded Usable and Unusable Speech Frames

  47. Preliminary Results

  48. Difference-Mean Comparison V/UV Classification Nonlinear Embedding Speech Nodal Density V/UV Classification Usable/Unusable Classification Summary

  49. Future Proposed Research • Determine optimum filter for nodal density-based voiced/unvoiced classification • Develop nodal density measure for usable/unusable classification • Investigate the presence of complimentary information in between both features (DMC and nodal density) for voiced/unvoiced classification • Perform decision-level fusion of both features

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