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

2016 | OriginalPaper | Chapter

Detecting Filled Pauses and Lengthenings in Russian Spontaneous Speech Using SVM

Authors : Vasilisa Verkhodanova, Vladimir Shapranov

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

Spontaneous speech differs from any other type of speech in many ways. And the presence of speech disfluencies is its prominent characteristic. These phenomena are important feature in human-human communication and at the same time a challenging obstacle for the speech processing tasks. This paper reports the experiment results on automatic detection of filled pauses and sound lengthenings basing on the automatically extracted acoustic features. We have performed machine learning experiments using support vector machine (SVM) classifier on the mixed and quality diverse corpus of Russian spontaneous speech. We applied Gaussian filtering and morphological opening to post-process the probability estimates from an SVM classifier. As the result we achieved F1–score of 0.54, with precision and recall being 0.55 and 0.53 respectively.

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previous chapter Designing Syllable Models for an HMM Based Speech Recognition System
next chapter Detecting Laughter and Filler Events by Time Series Smoothing with Genetic Algorithms
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Metadata
Title
Detecting Filled Pauses and Lengthenings in Russian Spontaneous Speech Using SVM
Authors
Vasilisa Verkhodanova
Vladimir Shapranov
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_26

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