Speech recognition system using enhanced mel frequency cepstral coefficient with windowing and framing method

Nowadays, speech recognition systems are used in various environments, namely, healthcare, robotics, vehicle control and unmanned aerial vehicle system. In recent years, many speech recognition systems have been developed to solve various issues in real world applications. We have proposed a novel speech recognition system using enhanced mel frequency cepstral coefficient with windowing and framing method. Windowing and framing method is used to remove the Gaussian white noise present in the input speech signal. The de-noising block effectively uses the nonnegative matrix factorization algorithm for factorizing the Mel-magnitude spectra of noisy input audio signal. Moreover, the mel-frequency cepstral coefficients (MFCC) is used for finding the more important features exist in the speech signal. Finally, Laplace smoothing technique is used as the language model for recognizing the audio signals. MATLAB software is used for demonstrating the proposed Mel frequency cepstral coefficient with Windowing and Framing based speech recognition system. We have compared the proposed speech recognition system with wavelet based feature extraction and artificial neural network based feature extraction methods for speech recognition. The experimental results proved the good performance of the proposed Mel frequency cepstral coefficient with windowing and framing based speech recognition system.