Kannada word recognition system using HTK

T. Ananthakrishna; M. Maithri; Kumara Shama

doi:10.1109/INDICON.2015.7443122

Kannada word recognition system using HTK

T. Ananthakrishna, M. Maithri, Kumara Shama

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

4 Citations (Scopus)

Abstract

In the present work, speech recognition system for Kannada language has been implemented using the Hidden Markov Tool Kit (HTK). The system performance is comparatively studied and evaluated for syllable and phone level models. The Kannada word dictionary of size about 110 words is used in the study and Mel frequency cepstral coefficients (MFCC) are computed in acoustic front-end processing. The system is designed to recognize isolated utterances of Kannada words, which are recorded from a Kannada short story. Baum-Welch algorithm is used to train the Hidden Markov Model (HMM) and Viterbi algorithm for decoding process. The objective of this study is to compare the performances of phone-level and syllable-level acoustical models for small to medium sized Kannada language vocabulary. The results are part of the on-going research work on large vocabulary continuous speech recognition system for Kannada language. Average word recognition accuracy of 97.1% for syllable-level modeling and 98.6% for phone-level modeling has been reported. Analysis of system performance also carried out based on the confusion matrices.

Original language	English
Title of host publication	12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control
Subtitle of host publication	(E3-C3), INDICON 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467373999
DOIs	https://doi.org/10.1109/INDICON.2015.7443122
Publication status	Published - 29-03-2016
Event	12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015 - New Delhi, India Duration: 17-12-2015 → 20-12-2015

Conference

Conference	12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015
Country/Territory	India
City	New Delhi
Period	17-12-15 → 20-12-15

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Systems Engineering
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/INDICON.2015.7443122

Cite this

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title = "Kannada word recognition system using HTK",

abstract = "In the present work, speech recognition system for Kannada language has been implemented using the Hidden Markov Tool Kit (HTK). The system performance is comparatively studied and evaluated for syllable and phone level models. The Kannada word dictionary of size about 110 words is used in the study and Mel frequency cepstral coefficients (MFCC) are computed in acoustic front-end processing. The system is designed to recognize isolated utterances of Kannada words, which are recorded from a Kannada short story. Baum-Welch algorithm is used to train the Hidden Markov Model (HMM) and Viterbi algorithm for decoding process. The objective of this study is to compare the performances of phone-level and syllable-level acoustical models for small to medium sized Kannada language vocabulary. The results are part of the on-going research work on large vocabulary continuous speech recognition system for Kannada language. Average word recognition accuracy of 97.1% for syllable-level modeling and 98.6% for phone-level modeling has been reported. Analysis of system performance also carried out based on the confusion matrices.",

author = "T. Ananthakrishna and M. Maithri and Kumara Shama",

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Ananthakrishna, T , Maithri, M & Shama, K 2016, Kannada word recognition system using HTK. in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015., 7443122, Institute of Electrical and Electronics Engineers Inc., 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015, New Delhi, India, 17-12-15. https://doi.org/10.1109/INDICON.2015.7443122

Kannada word recognition system using HTK. / Ananthakrishna, T.; Maithri, M.; Shama, Kumara.

12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7443122.

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

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T1 - Kannada word recognition system using HTK

AU - Ananthakrishna, T.

AU - Maithri, M.

AU - Shama, Kumara

PY - 2016/3/29

Y1 - 2016/3/29

N2 - In the present work, speech recognition system for Kannada language has been implemented using the Hidden Markov Tool Kit (HTK). The system performance is comparatively studied and evaluated for syllable and phone level models. The Kannada word dictionary of size about 110 words is used in the study and Mel frequency cepstral coefficients (MFCC) are computed in acoustic front-end processing. The system is designed to recognize isolated utterances of Kannada words, which are recorded from a Kannada short story. Baum-Welch algorithm is used to train the Hidden Markov Model (HMM) and Viterbi algorithm for decoding process. The objective of this study is to compare the performances of phone-level and syllable-level acoustical models for small to medium sized Kannada language vocabulary. The results are part of the on-going research work on large vocabulary continuous speech recognition system for Kannada language. Average word recognition accuracy of 97.1% for syllable-level modeling and 98.6% for phone-level modeling has been reported. Analysis of system performance also carried out based on the confusion matrices.

AB - In the present work, speech recognition system for Kannada language has been implemented using the Hidden Markov Tool Kit (HTK). The system performance is comparatively studied and evaluated for syllable and phone level models. The Kannada word dictionary of size about 110 words is used in the study and Mel frequency cepstral coefficients (MFCC) are computed in acoustic front-end processing. The system is designed to recognize isolated utterances of Kannada words, which are recorded from a Kannada short story. Baum-Welch algorithm is used to train the Hidden Markov Model (HMM) and Viterbi algorithm for decoding process. The objective of this study is to compare the performances of phone-level and syllable-level acoustical models for small to medium sized Kannada language vocabulary. The results are part of the on-going research work on large vocabulary continuous speech recognition system for Kannada language. Average word recognition accuracy of 97.1% for syllable-level modeling and 98.6% for phone-level modeling has been reported. Analysis of system performance also carried out based on the confusion matrices.

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ER -

Kannada word recognition system using HTK

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