Khasi Speech Recognition using Hidden Markov Model with Different Spectral Features: A Comparison

6 Pages Posted: 24 Jan 2020

See all articles by Fairriky Rynjah

Fairriky Rynjah

North Eastern Hill University

Bronson Syiem

North Eastern Hill University

L. Joyprakash Singh

North Eastern Hill University

Date Written: January 8, 2020

Abstract

In this work, various feature extraction techniques are implemented for a speech data corpus in Khasi Language. This paper provides a comparative performance analysis of widely used spectral features like Mel-Frequency Cepstrum Coefficients (MFCC), Perceptual Linear Prediction (PLP), Linear Predictive Cepstrum Coefficient (LPCC), Linear Predictive Coefficient (LPC) and Linear Predictive Reflection Coefficients (LPREFC). For each features we have used coefficient dimensions of static, delta and acceleration evaluated for the recognition of words spoken in Khasi language, a language spoken in the Khasi and Jaintia hills districts of Meghalaya. A recorder based continuous speech database has been collected as per the standard procedure and data processing was done using wave surfer. From the analysis, it was observed that for sampling rate of 16 KHz and 8 KHz the word recognition accuracy of MFCC and PLP features are better than LPCC, LPC and LPREFC features under all circumstances.

Keywords: Khasi Language, MFCC, PLP, LPCC, LPC, LPREFC

Suggested Citation

Rynjah, Fairriky and Syiem, Bronson and Singh, L. Joyprakash, Khasi Speech Recognition using Hidden Markov Model with Different Spectral Features: A Comparison (January 8, 2020). Available at SSRN: https://ssrn.com/abstract=3515823 or http://dx.doi.org/10.2139/ssrn.3515823

Fairriky Rynjah (Contact Author)

North Eastern Hill University ( email )

Umshing
Mawkynroh
Shillong, ME 793022
India

Bronson Syiem

North Eastern Hill University ( email )

Shillong
Meghalaya
India

L. Joyprakash Singh

North Eastern Hill University ( email )

Shillong
Meghalaya
India

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