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Published in:
Automatic Speech Recognition Based on Neural Networks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Phonetic Segmentation Procedure Based on Hidden Markov Models

Authors : Edvin Pakoci, Branislav Popović, Nikša Jakovljević, Darko Pekar, Fathy Yassa

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

In this paper, a novel variant of an automatic phonetic segmentation procedure is presented, especially useful if data is scarce. The procedure uses the Kaldi speech recognition toolkit as its basis, and combines and modifies several existing methods and Kaldi recipes. Both the specifics of model training and test data alignment are explained in detail. Effectiveness of artificial extension of the starting amount of manually labeled material during training is examined as well. Experimental results show the admirable overall correctness of the proposed procedure in the given test environment. Several variants of the procedure are compared, and the usage of speaker-adapted context-dependent triphone models trained without the expanded manually checked data is proven to produce the best results. A few ways to improve the procedure even more, as well as future work, are also discussed.

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Metadata
Title
A Phonetic Segmentation Procedure Based on Hidden Markov Models
Authors
Edvin Pakoci
Branislav Popović
Nikša Jakovljević
Darko Pekar
Fathy Yassa
Copyright Year
2016
Book
Speech and Computer

Print ISBN: 978-3-319-43957-0

Electronic ISBN: 978-3-319-43958-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-43958-7_7

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