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Paper Details

Paper Title
Modified MFCC Algorithm for Speech Recognition
Automatic Speech Recognition has been an active research topic for more than four decades. With the advent of digital computing and signal processing, the problem of speech recognition was clearly posed and thoroughly studied .These developments were complemented with an increased awareness of the advantages of conversational systems. The goal of Automatic Speech Recognition is to develop techniques and systems that enable computers to accept speech input. The speech recognition problem may be interpreted as speech to text conversion problem. Since the early 80s,compact implementations of accurate, real-time speech recognizers have found wide spread applications, which includes voice activated transcription, simplified man machine communication, aids for hearing impaired individuals and the physically disabled telephone assistance and other man-machine interface tasks. The aim of this work is to develop a speech recognition (SR) system based on Vector Quantization (VQ) approach. This system receives speech inputs from users, analyzes the speech inputs, searches and matches the input speech with the pre-recorded and stored speeches in the trained database or codebook. First the feature extraction from the speech signal is done by a parameterization of the wave formed signal into relevant feature vectors by Mel Frequency Cepstral Coefficients (MFCC ) algorithm and Modified MFCC .This parametric form is then used by the recognition system both in training the models and testing.
Speech Recognition, Vector Quantization, Mel Frequency Cepstral Coefficients (MFCC ), Feature Extraction, voice activated transcription.
Others Details
Paper Id : 46102
Author Name : D.SRINIVAS
Co-Author Name(s) : B ARUNAN PrakashRavi Boda
Volume/Issue No : Volume 05 Issue 02
Page No : 51-55
DOI Number : DOI:10.21090/IJAERD.46102
Publication Date : 2018-02-08
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website :
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406