Automatic speech recognition using Mel- frequency cepstrum coefficient (MFCC) and vector quantization (VQ) techniques for continuous speech

Abstract

Automatic speech recognition is a field related to the interaction between user and machine using effective techniques. ASR is one of the very hot concepts in these days. A lot of researchers worked on different techniques to achieve the best accuracy for speech recognition. In previous research techniques used provides accuracy for a single utterance. Due to which for continuous utterance combination of the technique used in this research work which provides best accurate performance with less noisy interaction. For this research work, Mel Frequency Cepstrum Coefficient (MFCC) and Vector Quantization (VQ) techniques are used. These techniques provide easy speech processing with Mel-frequency scale which includes spacing of linear frequency less than 1000 Hz. Due to which MFCC provides high accuracy, less complexity and high performance with capturing main characteristics of speech. This approach provides efficient and more accurate results than other techniques for a continuous speech by minimizing the distortion created by noise. In this research work algorithms for each technique are represented. This research work presents best possible accuracy for continuous speech signal as compared to other feature extraction techniques.

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Speaker ethnic identification for continuous speech in malay language using pitch and MFCC

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