Robust speech recognition using MLP neural network in log-spectral domain
Masoumeh P. Ghaemmaghami, Hossein Sameti, Farbod Razzazi, Bagher BabaAli, Saeed Dabbaghchian · 2009
In this paper, we have proposed an efficient and effective nonlinear feature domain noise suppression algorithm, motivated by the minimum mean square error (MMSE) optimization criterion. A Multi Layer Perceptron (MLP) neural network in the log spectral domain has been employed to minimize the difference between noisy and clean speech. By using this method, as a pre-processing stage of a speech recognition system, the recognition rate in noisy environments has been improved. We extended the application of the system to different environments with different noises without retraining HMM model. We trained the feature extraction stage with a small portion of noisy data which was created by artificially adding different types of noises from the NOISEX-92 database to the TIMIT speech database. In real environment, where our speech recognition systems must work, different types of noises with various SNRs exist. Our proposed method suggests four strategies based on the system capability to identify the noise type and SNR. Experimental results show that the proposed method achieves significant improvement in recognition rates.