Robustness Techniques for Feature Extraction - Berlin Chen

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Additive Noises • Additive noises can be stationary or non-stationary – Stationary noises • Such as computer fan, air conditioning, car noise: the power spectral density does not change over time (the above noises are also narrow-band noises) – Non-stationary noises • Machine gun, door slams, keyboard clicks, radio/TV, and other speakers’ voices (babble noise, wide band nose, most difficult): the statistical properties change over time 6
Additive Noises 7
Page 1 and 2: Robustness Techniques for Speech Re
Page 3 and 4: Introduction • The Diagram for Sp
Page 5: The Noise Types s[m] h[m] n[m] x[m]
Page 9 and 10: Noise Characteristics • White Noi
Page 11 and 12: Robustness Approaches
Page 13 and 14: Three Basic Categories of Approache
Page 15 and 16: Spectral Subtraction (SS) • Probl
Page 17 and 18: Spectral Subtraction (SS) • Spect
Page 19 and 20: Wiener Filtering • Minimize the e
Page 21 and 22: Wiener Filtering • The time varyi
Page 23 and 24: • Approach - II : Wiener Filterin
Page 25 and 26: Wiener Filtering Clean Speech Noisy
Page 27 and 28: Cepstral Mean Normalization (CMN)
Page 33 and 34: RASTA Temporal Filter • The IIR t
Page 35 and 36: Retraining on Corrupted Speech •
Page 37 and 38: Signal Decomposition Using HMMs •
Page 39 and 40: Parallel Model Combination (PMC)
Page 41 and 42: Parallel Model Combination (PMC)
Page 43 and 44: Vector Taylor Series (VTS) P. J. Mo
Page 45 and 46: Vector Taylor Series (VTS) • Appl
Page 47 and 48: Retraining on Compensated Features
Page 49 and 50: Principal Component Analysis 49
Page 51 and 52: PCA Applied in Inherently Robust Fe
Page 57 and 58:
Linear Discriminative Analysis •
Page 59 and 60:
Minimum Classification Error • Mi
Page 61 and 62:
Minimum Classification Error • Mi
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Robustness Techniques for Feature Extraction - Berlin Chen

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