Abstract
We present a method for sequentially estimating time-varying noise parameters. Noise parameters are sequences of time-varying mean vectors representing the noise power in the log-spectral domain. The proposed sequential Monte Carlo method generates a set of particles in compliance with the prior distribution given by clean speech models. The noise parameters in this model evolve according to random walk functions and the model uses extended Kalman filters to update the weight of each particle as a function of observed noisy speech signals, speech model parameters, and the evolved noise parameters in each particle. Finally, the updated noise parameter is obtained by means of minimum mean square error (MMSE) estimation on these particles. For efficient computations, the residual resampling and Metropolis-Hastings smoothing are used. The proposed sequential estimation method is applied to noisy speech recognition and speech enhancement under strongly time-varying noise conditions. In both scenarios, this method outperforms some alternative methods.
Highlights
A speech processing system may be required to work in conditions where the speech signals are distorted due to background noise
In terms of recognition performance in the simulated nonstationary noise described in Section 4.2, Table 2 shows that the method can effectively improve system robustness to the time-varying noise
We have presented a sequential Monte Carlo method for a Bayesian estimation of time-varying noise parameters
Summary
A speech processing system may be required to work in conditions where the speech signals are distorted due to background noise. Those distortions can drastically drop the performance of automatic speech recognition (ASR) systems, which usually perform well in quiet environments. One approach is based on front-end processing of speech signals, for example, speech enhancement. Speech enhancement can be done either in time-domain, for example, in [1, 2], or more widely used, in spectral domain [3, 4, 5, 6, 7]. The objective of speech enhancement is to increase signal-to-noise ratio (SNR) of the processed speech with respect to the observed noisy speech signal
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