Automatic Speech Recognition Using Template Model for Man-Machine Interface

TL;DR

This paper presents a speech recognition system that uses MFCC for feature extraction and DTW for pattern matching, aiming to improve natural man-machine communication.

Contribution

It introduces a template model approach combining MFCC and DTW for speech recognition, enhancing interaction capabilities.

Findings

Effective speech recognition with MFCC and DTW

Improved accuracy in pattern matching

Potential for natural man-machine interfaces

Abstract

Speech is a natural form of communication for human beings, and computers with the ability to understand speech and speak with a human voice are expected to contribute to the development of more natural man-machine interfaces. Computers with this kind of ability are gradually becoming a reality, through the evolution of speech recognition technologies. Speech is being an important mode of interaction with computers. In this paper Feature extraction is implemented using well-known Mel-Frequency Cepstral Coefficients (MFCC).Pattern matching is done using Dynamic time warping (DTW) algorithm.