By Tuomas Virtanen
Computerized speech acceptance (ASR) platforms are discovering expanding use in daily life. a few of the ordinary environments the place the structures are used are noisy, for instance clients calling up a voice seek approach from a hectic cafeteria or a road. this may bring about degraded speech recordings and adversely have an effect on the functionality of speech acceptance structures. because the use of ASR platforms raises, wisdom of the cutting-edge in strategies to accommodate such difficulties turns into serious to method and alertness engineers and researchers who paintings with or on ASR applied sciences. This publication offers a complete survey of the cutting-edge in thoughts used to enhance the robustness of speech reputation structures to those degrading exterior affects.
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Extra resources for Techniques for Noise Robustness in Automatic Speech Recognition
Example text
Will not affect the Bayes error, that is Lf (x) = Lx . , measurements of observations from the class). , noise). Let z = x + y . The Bayes error of a classifier based on z will be no lower than the Bayes error of x alone, that is Lz ≥ Lx . In practice, Lz will usually be greater than Lx . 1 illustrates this pictorially. 1 Here, we have explicitly used the notation P (c|x = X) to represent the a posteriori probability of c given that the feature x takes the value X, and P (c|f (x) = f (X)) to represent the a posteriori probability of c given that the f (x) takes the value f (X).
23) is the ratio of the total expected number of transitions from state i to j, and the total expected number of times the process was in state i. 24) represents the expected number of times the observation at time t in X was drawn from the kth Gaussian of the state output distribution of i. 25) is similarly the ratio of the expected number of observations generated from the kth Gaussian of i to the expected total number of observations from i. We note that all of these equations are intuitive extensions of familiar count-based estimation of probabilities in multinomial data.
4 The effect of saturation. 3 when it has been distorted by saturation resulting from excessive gain in the amplifier. Saturation is often visually apparent in the envelope of the signal. Bottom: The spectrogram of the saturated signal, after it has been digitized at 16 kHz. Saturation results in high-frequency components that get aliased into spurious spectrographic patterns in the 0–8 kHz frequency band. Note that the spurious patterns are chiefly visible in regions where the signal amplitude was high enough to be affected by the saturation.