Speech Enhancement Using MMSE Estimation and Spectral Subtraction Methods

Efficiency of the speech recognition system in noise free environment is impressive but in the presence of environmental noise the efficiency of the speech recognition system deteriorates drastically. Environmental noise also affects human-to-human or human-to-machine communications and degrades the speech quality as well as intelligibility. Here a speech recognition system is proposed in presence of noisy environment. Database of ten Hindi digits was prepared for fifty speakers. Speech and F16 noises were added to clean database to make the noisy database at different Signal-to-Noise Ratio (SNR) levels (-5dB, 0dB, 5dB, 10dB). Spectral estimation techniques like Spectral Subtraction (SS) and Minimum Mean Square Error (MMSE) estimation based methods were used for de-noising the speech before feature extraction. Mel Frequency Cepstral Coefficient (MFCC) and Hidden Markov Model (HMM) were used as feature extraction technique and classifier respectively. Multi-band SS de-noising approach has shown best recognition results as compared to all other techniques for both types of noises.