Performance analysis of hybrid robust automatic speech recognition system
C. Ganesh Babu, R. Hari Kumar, Ponnusamy Thangapandian Vanathi · 2012
In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy input speech signal through the Hybrid method using Voice Activity Detection (VAD) and Speech Enhancement Algorithm (SEA). Demand for Speech Recognition technology is expected to rise dramatically over the next few years as people use their mobile phones and voice recognition system everywhere. This paper enlighten the implementation process which includes a speech-to-text system using isolated word recognition with a vocabulary of ten words (digits 0 to 9). In the training period, the uttered digits are recorded using 8-bit Pulse Code Modulation (PCM) with a sampling rate of 8 KHz and save as a wave format file using sound recorder software. For a given word in the vocabulary, the system builds an Hidden Markov Model (HMM) model and trains the model during the training phase. The training steps, from VAD, Speech Enhancement to HMM model building, are performed using PC-based Matlab programs.