Linear Prediction of Speech
Subjective comparison and evaluation of speech enhancement algorithms
Speech Communication
IEEE Transactions on Information Theory
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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 process speech signals easily and also recognize the text. Demand for speech recognition technology is expected to raise dramatically over the next few years as people use their mobile phones as all purpose lifestyle devices. In this paper, an implementation of a speech-to-text system 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-bit Pulse Code Modulation (PCM) with a sampling rate of 8 KHz and saved as a wave file using sound recorder software. The system performs speech analysis using the Linear Predictive Coding (LPC) method of degree. From the LPC coefficients, the weighted cepstral coefficients and cepstral time 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 vecttor for a frame is arrived. Then, the system performs 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-based Matlab 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.