A novel model characteristics for noise-robust Automatic Speech Recognition based on HMM

This paper proposes a new model for a noise-robust Automatic Speech Recognition (ASR) based on parallel branch Hidden Markov Model (HMM) structure with a novel approach for robust speech recognition. Automatic Speech Recognition applications such as voice command and control, audio indexing, speech-to-speech translation, do not usually work well in noisy environments. In this paper, we present the characteristics of a novel model by exploring vibrocervigraphic and electromyographic ASR methods and some other effective approaches to achieve the best results. By employing the proposed model, we obtain the word error rate, available bandwidths with cutoff frequencies, word recognition rate, etc. This paper includes advanced front-end processing with less computational requirements and a statistical modeling for large-vocabulary myoelectric speech. Therefore parameters estimation of ASR system like Mel-Frequency Cepstral Coefficients (MFCC) are investigated to create the statistically optimized model.