Robustness Techniques for Feature Extraction - Berlin Chen

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Convolutional Noises • Convolutional noises are mainly resulted from channel distortion (sometimes called “channel noises”) and are stationary <strong>for</strong> most cases – Reverberation, the frequency response of microphone, transmission lines, etc. 8
Noise Characteristics • White Noise – The power spectrum is flat S nn ω = ,a condition equivalent to different samples being uncorrelated, R nn [ m] = qδ [ m] – White noise has a zero mean, but can have different distributions – We are often interested in the white Gaussian noise, as it resembles better the noise that tends to occur in practice • Colored Noise ( ) q – The spectrum is not flat (like the noise captured by a microphone) – Pink noise • A particular type of colored nose that has a low-pass nature, as it has more energy at the low frequencies and rolls off at high frequency • E.g., the noise generated by a computer fan, an air conditioner, or an automobile 9
Page 1 and 2: Robustness Techniques for Speech Re
Page 3 and 4: Introduction • The Diagram for Sp
Page 5 and 6: The Noise Types s[m] h[m] n[m] x[m]
Page 7: Additive Noises 7
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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