2.5.4.1 Basics of Neural Networks

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

43. روش MFCC • روش MFCC مبتني بر نحوه ادراک گوش انسان از اصوات مي باشد. • روش MFCC نسبت به ساير ويژگِيها در محيطهاي نويزي بهتر عمل ميکند. • MFCC اساساً جهت کاربردهاي شناسايي گفتار ارايه شده است اما در شناسايي گوينده نيز راندمان مناسبي دارد. • واحد شنيدار گوش انسان Mel مي باشد که به کمک رابطه زير بدست مي آيد:

44. مراحل روش MFCC مرحله 1: نگاشت سيگنال از حوزه زمان به حوزه فرکانس به کمک FFT زمان کوتاه. : سيگنال گفتارZ(n) : تابع پنجره مانند پنجره همينگW(n( WF= e-j2π/F m : 0,…,F – 1; : طول فريم گفتاري.F

45. مراحل روش MFCC مرحله 2: يافتن انرژي هر کانال بانک فيلتر. که M تعداد بانکهاي فيلتر مبتني بر معيار مل ميباشد. تابع فيلترهاي بانک فيلتر است.

46. توزيع فيلتر مبتنی بر معيار مل

47. مراحل روش MFCC • مرحله 4: فشرده سازي طيف و اعمال تبديل DCT جهت حصول به ضرايب MFCC • در رابطه بالا L،...،0=n مرتبه ضرايب MFCC ميباشد.

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

49. 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

50. 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