A comparison of Gaussian Mixture Modeling (GMM) and Hidden Markov Modeling (HMM) based approaches for Automatic Phoneme Recognition in Kannada

Prashanth Kannadaguli; Vidya Bhat

doi:10.1109/ICSPCom.2015.7150658

A comparison of Gaussian Mixture Modeling (GMM) and Hidden Markov Modeling (HMM) based approaches for Automatic Phoneme Recognition in Kannada

Prashanth Kannadaguli, Vidya Bhat

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Citations (Scopus)

Abstract

We build and compare phoneme recognition systems based on Gaussian Mixture Modeling (GMM) which is a static modeling scheme and Hidden Markov Modeling (HMM) which is a Dynamic modeling scheme. Both models were built by using Stochastic pattern recognition and Acoustic phonetic schemes to recognise phonemes. Since our native language is Kannada, a rich South Indian Language, we have used 15 Kannada phonemes to train and test these models. Since Mel-Frequency Cepstral Coefficients (MFCC) are well known Acoustic features of speech, we have used the same in speech feature extraction. Finally performance analysis of both models in terms of Phoneme Error Rate (PER) justifies the fact that Dynamic modeling yields better results over Static modeling and can be used in developing Automatic Speech Recognition systems.

Original language	English
Title of host publication	2015 International Conference on Signal Processing and Communication, ICSC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	257-260
Number of pages	4
ISBN (Electronic)	9781479967612
DOIs	https://doi.org/10.1109/ICSPCom.2015.7150658
Publication status	Published - 01-01-2015
Externally published	Yes
Event	2015 International Conference on Signal Processing and Communication, ICSC 2015 - Noida, India Duration: 16-03-2015 → 18-03-2015

Conference

Conference	2015 International Conference on Signal Processing and Communication, ICSC 2015
Country/Territory	India
City	Noida
Period	16-03-15 → 18-03-15

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing

Access to Document

10.1109/ICSPCom.2015.7150658

Cite this

Kannadaguli, P., & Bhat, V. (2015). A comparison of Gaussian Mixture Modeling (GMM) and Hidden Markov Modeling (HMM) based approaches for Automatic Phoneme Recognition in Kannada. In 2015 International Conference on Signal Processing and Communication, ICSC 2015 (pp. 257-260). [7150658] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSPCom.2015.7150658

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Kannadaguli, P & Bhat, V 2015, A comparison of Gaussian Mixture Modeling (GMM) and Hidden Markov Modeling (HMM) based approaches for Automatic Phoneme Recognition in Kannada. in 2015 International Conference on Signal Processing and Communication, ICSC 2015., 7150658, Institute of Electrical and Electronics Engineers Inc., pp. 257-260, 2015 International Conference on Signal Processing and Communication, ICSC 2015, Noida, India, 16-03-15. https://doi.org/10.1109/ICSPCom.2015.7150658

A comparison of Gaussian Mixture Modeling (GMM) and Hidden Markov Modeling (HMM) based approaches for Automatic Phoneme Recognition in Kannada. / Kannadaguli, Prashanth; Bhat, Vidya.

2015 International Conference on Signal Processing and Communication, ICSC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 257-260 7150658.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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A comparison of Gaussian Mixture Modeling (GMM) and Hidden Markov Modeling (HMM) based approaches for Automatic Phoneme Recognition in Kannada

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