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Voice Recognition by a Realistic Model of Biological Neural Networks

Voice Recognition by a Realistic Model of Biological Neural Networks. by Efrat Barak Supervised by Karina Odinaev Igal Raichelgauz. Structure . Project Objective The Model The Classification Process Results & Analysis Conclusion. Project Objective.

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Voice Recognition by a Realistic Model of Biological Neural Networks

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  1. Voice Recognition by a Realistic Model of Biological Neural Networks by Efrat Barak Supervised by Karina Odinaev Igal Raichelgauz

  2. Structure • Project Objective • The Model • The Classification Process • Results & Analysis • Conclusion

  3. Project Objective Configure a neural network based system for voice recognition

  4. The Model

  5. The Main Principle The readout function recognizes the basin that the network has converged to, and classifies the input according to the indicator of that basin

  6. Correspondence with the Theory of Attractor Neural Networks • The system converges to a basin • The basins are periodic attractors

  7. Correspondence with the LSM theory • The neural network may be treated as a liquid • The readout function receives only the current state of the liquid and transforms it to an output signal • The system can perform several tasks simultaneously

  8. Neural Network Structure • 22 Input Neurons • 135 spiking neurons in a 3x3x15 formation • LIF model for neurons behavior • 20% of the neurons are inhibitory and 80% of them are excitatory • Dynamic synapses

  9. Creating the Stimulus 30 seconds of recorded speech are encoded into 1 second of spike trains, in the following methods: • Time Encoding – A straight forward conversion

  10. Creating the Stimulus • Mel Frequency Cepstral Coefficients (MFCCs) encoding - In this method the frequency bands are positioned logarithmically, on the mel scale. A periodic spikes train is added to the second of the voice segment.

  11. Performing a Simulation • A new network is created • A stimulus of one speech segment is fed to the network, followed by a periodic driving force (Repeated for every combination of segment and frequency). • The basins are categorized by their activity vector.

  12. The Classification Process

  13. The Indicators Map • - The number of segments of the wanted voice that converged to the basin b. • - The number of segments of the unwanted voice that converged to the basin b. • - The total number of initials that converged to the basin b.

  14. The Indicators Map The indicator of basin b:

  15. The Indicators Map Examples:

  16. The Indicators Map

  17. The Indicators Map Indicators’ Average:

  18. The Classification Process

  19. Tuning Step 1. Select frequencies

  20. Tuning Preceding to Step 2. Why do we need a threshold?

  21. Tuning Step 2. Determine the threshold

  22. The Classification Process

  23. Results – Amplitude Encoded Input Input Examples Wanted Voice Unwanted Voice

  24. Results – Amplitude Encoded Input Results of a verification test

  25. Results – Amplitude Encoded Input Results of a Classification Test

  26. Results – Amplitude Encoded Input Results of Classification by Two Different Systems

  27. Results – Amplitude Encoded Input Cross Classification

  28. Results – Amplitude Encoded Input Results of cross classification for systems 1 and 2: 50.2% Answered , 49.8% Unanswered

  29. Results – MFCC Encoded Input Input Examples Wanted Voice Unwanted Voice

  30. Results – MFCC Encoded Input Results of a classification test Two sets of new data were used

  31. Results – MFCC Encoded Input

  32. Basins Creation Pattern (a) 324 initials (b) 100 initials (c) 60 initials

  33. Conclusion • A system for voice recognition, based on neuro-computations, was designed • The system succeeded in recognizing the wanted voice when the input was encoded by its amplitude.

  34. Conclusion • The MFCC method yielded very different inputs, therefore the ability of the system to recognize such input was proven partially. • The system’s stability was proved

  35. Suggestions for Future Projects • Prepare the system for various types of inputs • Perform automatic tuning by using statistical tools • Prove that the system can perform several tasks simultaneously

  36. THE END

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