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SUMMARYThis paper presents a novel noise-robust feature extraction method for speech recognition. It is based on making the Minimum Variance Distortionless Response (MVDR) power spectrum estimation method robust against noise. This robustness is obtained by modifying the distortionless constraint of the MVDR spectral estimation method via weighting the sub-band power spectrum values based on the sub-band signal to noise ratios. The optimum weighting is obtained by employing the experimental findings of psychoacoustics. According to our experiments, this technique is successful in modifying the power spectrum of speech signals and making it robust against noise. The above method, when evaluated on Aurora 2 task for recognition purposes, outperformed both the MFCC features as the baseline and the MVDR-based features in different noisy conditions.

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This paper presents a new method for speech enhancement based on a dictionary learning method. The proposed approach is based on using coherence measure in dictionary learning. Data required for better fitting to atoms in sparse representation of noise is provided by a noise estimation algorithm that causes noise dictionary to be trained with the same data size as speech signal. To decrease coherence between dictionaries after the training step, a new method is applied to yield incoherent dictionaries. In sparse representation of speech data, the highest energy atoms of noise dictionary are replaced with the lowest energy atoms, under certain conditions. A similar replacement can happen in sparse representation of noise data. Furthermore, in this paper, only one noise dictionary, chosen by a classification method, is used in speech enhancement step, resulting in a faster algorithm. Objective and subjective measures are used for evaluating the simulation results. According to experimental results, the proposed algorithm has been found superior in performance and computation overhead in comparison with the earlier methods in this context. Moreover, this method achieves significantly better results compared with baseline methods such as multiband and geometric spectral subtraction.

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