TY - GEN
T1 - Kannada word recognition system using HTK
AU - Ananthakrishna, T.
AU - Maithri, M.
AU - Shama, Kumara
N1 - Publisher Copyright:
© 2015 IEEE.
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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U2 - 10.1109/INDICON.2015.7443122
DO - 10.1109/INDICON.2015.7443122
M3 - Conference contribution
AN - SCOPUS:84994285906
T3 - 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015
BT - 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015
Y2 - 17 December 2015 through 20 December 2015
ER -
