Spotting multilingual consonant-vowel units of speech using neural network models

Authors:
Suryakanth V. Gangashetty;C. Chandra Sekhar;B. Yegnanarayana
Affiliations:
Speech and Vision Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India;Speech and Vision Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India;Speech and Vision Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India
Venue:
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Year:
2005

Citing 6
Cited 1

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Recognition of Consonant-Vowel (CV) Units of Speech in a Broadcast News Corpus Using Support Vector Machines

SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines

Recognition of consonant-vowel (CV) units under background noise using combined temporal and spectral preprocessing

International Journal of Speech Technology

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Abstract

Multilingual speech recognition system is required for tasks that use several languages in one speech recognition application. In this paper, we propose an approach for multilingual speech recognition by spotting consonant-vowel (CV) units. The important features of spotting approach are that there is no need for automatic segmentation of speech and it is not necessary to use models for higher level units to recognise the CV units. The main issues in spotting multilingual CV units are the location of anchor points and labeling the regions around these anchor points using suitable classifiers. The vowel onset points (VOPs) have been used as anchor points. The distribution capturing ability of autoassociative neural network (AANN) models is explored for detection of VOPs in continuous speech. We explore classification models such as support vector machines (SVMs) which are capable of discriminating confusable classes of CV units and generalisation from limited amount of training data. The data for similar CV units across languages are shared to train the classifiers for recognition of CV units of speech in multiple languages. We study the spotting approach for recognition of a large number of CV units in the broadcast news corpus of three Indian languages.