Inductive String Template-Based Learning of Spoken Language

Alexander Gutkin, Simon King

Abstract

This paper deals with formulation of alternative structural approach to the speech recognition problem. In this approach, we require both the representation and the learning algorithms defined on it to be linguistically meaningful, which allows the speech recognition system to discover the nature of the linguistic classes of speech patterns corresponding to the speech waveforms. We briefly discuss the current formalisms and propose an alternative — a phonologically inspired string-based inductive speech representation, defined within an analytical framework specifically designed to address the issues of class and object representation. We also present the results of the phoneme classification experiments conducted on the TIMIT corpus of continuous speech.

References

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Paper Citation


in Harvard Style

Gutkin A. and King S. (2005). Inductive String Template-Based Learning of Spoken Language . In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005) ISBN 972-8865-28-7, pages 43-51. DOI: 10.5220/0002568800430051


in Bibtex Style

@conference{pris05,
author={Alexander Gutkin and Simon King},
title={Inductive String Template-Based Learning of Spoken Language},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)},
year={2005},
pages={43-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002568800430051},
isbn={972-8865-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)
TI - Inductive String Template-Based Learning of Spoken Language
SN - 972-8865-28-7
AU - Gutkin A.
AU - King S.
PY - 2005
SP - 43
EP - 51
DO - 10.5220/0002568800430051