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Author Manaileng, M.J. ♦ Manamela, M.J.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Physics ♦ Electricity & electronics ♦ Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword hidden Markov model toolkit ♦ automatic speech recognition ♦ cepstral mean variance normalization ♦ hidden Markov models ♦ Accuracy ♦ Cepstral analysis ♦ Hidden Markov models ♦ cepstral variance normalization ♦ Speech recognition ♦ Speech ♦ Feature extraction ♦ Robustness ♦ cepstral mean normalization
Abstract This paper presents the development of a speech recognition system for automatically recognizing fluently spoken digit strings in Northern Sotho. The digit strings can be isolated or connected/continuous with known or unknown length. The digit recognition system has been trained with the aim of satisfying its potential end-users. Our main research focus was to enhance the robustness of a connected-digits recognizer such that it can handle continuous speech input restricted to numeric digits vocabularies. The Hidden Markov Model Toolkit (HTK) was used for experimentation. The standard technique that is based on the use of hidden Markov models (HMMs) was augmented with Cepstral Mean Vector Normalization (CMVN); a technique designed to handle convoluted distortions with the aim of increasing the robustness of speech recognition systems. A 1255 words dataset extracted from an existing general-purpose Northern Sotho speech database collected from mother tongue speakers between the ages of 16 and 60 was used in our experiment. The CMVN technique obtained a phone recognition accuracy of 75.84% and a word recognition accuracy of 62.30% whereas the standard HMM-based technique obtained phone recognition accuracy of 72.45% and a word recognition accuracy of 4.57%.
Description Author affiliation: Dept. of Comput. Sci., Univ. of Limpopo, Sovenga, South Africa (Manaileng, M.J.; Manamela, M.J.)
ISBN 9789537044145
ISSN 13342630
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-09-25
Publisher Place Croatia
Rights Holder Croatian Society Electronics in Marine - ELMAR
e-ISBN 9789537044145
Size (in Bytes) 203.41 kB
Page Count 4
Starting Page 211
Ending Page 214

Source: IEEE Xplore Digital Library