Performance Analysis of Speech Enhancement Algorithm for Robust Speech Recognition System

Widely Speech Signal Processing has not been used much in the field of electronics and computers due to the complexity and variety of speech signals and sounds with the advent of new technology. However, with modern processes, algorithms, and methods which can proc Demand for speech recognition technology is expected to their mobile phones as all purpose lifestyle devices. In this paper, an implementation o using isolated word recognition with a vocabulary of ten words (digits 0 to 9 with each 100 samples) and statistical modeling (Hidden Markov Model HMM) for machine speech recognition was undertaken. In the training phase, the uttered digits are recorded using 8 as a wave file using sound recorder software. The system performs speech analysis using the Linear Predictive Coding (LPC) method of degree. From the derivatives are derived. From these variables the feature vector for a frame is arrived. Then, the system performs Vector Quantization (VQ) utilizing a vector codebook which result vect given word in the vocabulary, the system builds an HMM model and trains the model during the training phase. The training steps, from Speech Enhancement to HMM model building, are performed using PC programs. Our current framework uses a speech processing module includes Speech Enhancement algorithm with Hidden Markov Model (HMM)-based classification and noise language modeling to achieve effective noise knowledge estimation. Key-Words: Hidden Markov Model, Vector Quantization, Speech Enhancement, Linear Predictive Coding, Speech Recognition.