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Advanced Techniques in Neural Networks for Speech Recognition

Explore neural network topologies, structures, and spectral analysis models for speech recognition. Learn about filter bank implementation and practical examples in speech processing. Discover signal processing methods like Mel-scaling, FFT, IDCT, and Cepstra analysis.

noah-lowe
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Advanced Techniques in Neural Networks for Speech Recognition

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  1. 2.5.4.1 Basics of Neural Networks

  2. 2.5.4.2 Neural Network Topologies

  3. 2.5.4.2 Neural Network Topologies

  4. 2.5.4.2 Neural Network Topologies

  5. TDNN

  6. 2.5.4.6 Neural Network Structures for Speech Recognition

  7. 2.5.4.6 Neural Network Structures for Speech Recognition

  8. 3.1.1 Spectral Analysis Models

  9. 3.1.1 Spectral Analysis Models

  10. 3.2 THE BANK-OF-FILTERS FRONT- END PROCESSOR

  11. 3.2 THE BANK-OF-FILTERS FRONT- END PROCESSOR

  12. 3.2 THE BANK-OF-FILTERS FRONT- END PROCESSOR

  13. 3.2 THE BANK-OF-FILTERS FRONT- END PROCESSOR

  14. 3.2 THE BANK-OF-FILTERS FRONT- END PROCESSOR

  15. 3.2.1 Types of Filter Bank Used for Speech Recognition

  16. Nonuniform Filter Banks

  17. Nonuniform Filter Banks

  18. 3.2.1 Types of Filter Bank Used for Speech Recognition

  19. 3.2.1 Types of Filter Bank Used for Speech Recognition

  20. 3.2.2 Implementations of Filter Banks • Instead of direct convolution, which is computationally expensive, we assume each bandpass filter impulse response to be represented by: Where w(n) is a fixed lowpass filter

  21. 3.2.2 Implementations of Filter Banks

  22. 3.2.2.1 Frequency Domain Interpretation of the Short-Time Fourier Transform

  23. 3.2.2.1 Frequency Domain Interpretation of the Short-Time Fourier Transform

  24. 3.2.2.1 Frequency Domain Interpretation of the Short-Time Fourier Transform

  25. 3.2.2.1 Frequency Domain Interpretation of the Short-Time Fourier Transform

  26. Linear Filter Interpretation of the STFT

  27. 3.2.2.4 FFT Implementation of a Uniform Filter Bank

  28. Direct implementation of an arbitrary filter bank

  29. 3.2.2.5 Nonuniform FIR Filter Bank Implementations

  30. 3.2.2.7 Tree Structure Realizations of Nonuniform Filter Banks

  31. 3.2.4 Practical Examples of Speech-Recognition Filter Banks

  32. 3.2.4 Practical Examples of Speech-Recognition Filter Banks

  33. 3.2.4 Practical Examples of Speech-Recognition Filter Banks

  34. 3.2.4 Practical Examples of Speech-Recognition Filter Banks

  35. 3.2.5 Generalizations of Filter-Bank Analyzer

  36. 3.2.5 Generalizations of Filter-Bank Analyzer

  37. 3.2.5 Generalizations of Filter-Bank Analyzer

  38. 3.2.5 Generalizations of Filter-Bank Analyzer

  39. سیگنال زمانی Mel-scaling فریم بندی |FFT|2 Logarithm IDCT Cepstra Low-order coefficients Delta & Delta Delta Cepstra Differentiator روش مل-کپستروم

  40. Time-Frequency analysis • Short-term Fourier Transform • Standard way of frequency analysis: decompose the incoming signal into the constituent frequency components. • W(n): windowing function • N: frame length • p: step size

  41. Critical band integration • Related to masking phenomenon: the threshold of a sinusoid is elevated when its frequency is close to the center frequency of a narrow-band noise • Frequency components within a critical band are not resolved. Auditory system interprets the signals within a critical band as a whole

  42. Bark scale

  43. Feature orthogonalization • Spectral values in adjacent frequency channels are highly correlated • The correlation results in a Gaussian model with lots of parameters: have to estimate all the elements of the covariance matrix • Decorrelation is useful to improve the parameter estimation.

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