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Authors: Fernando Perdigão 1 ; Cláudio Neves 2 and Luís Sá 1

Affiliations: 1 Instituto de Telecomunicações – Pole of Coimbra and University of Coimbra, Portugal ; 2 Instituto de Telecomunicações – Pole of Coimbra, Portugal

ISBN: 978-989-8425-89-8

Keyword(s): Continuous Speech, Unvoiced Speech, Acoustic signal Discrimination.

Related Ontology Subjects/Areas/Topics: Acoustic Signal Processing ; Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Identification of voice pathologies using only the voice signal has a great advantage over the conventional methods, such as laryngoscopy, since they enable a non-invasive diagnosis. The first studies in this area were based on the analysis of sustained vowel sounds. More recently, there are studies that extend the analysis to continuous speech, achieving similar or better results. All these studies use of a pitch detector algorithm to select only the voiced parts of the acoustic signal. However, the existence of a pathology affecting the speaker’s vocal folds produces a more irregular vibration pattern and, consequently, a degradation of the voice quality with less voiced segments. Thus, by selecting only clear voiced segments for the classifier, useful pathological information may be disregarded. In this study we propose a new approach that enables the classification of voice pathology by also analyzing the unvoiced information of continuous speech. The signal frames are divided in turbulent/non-turbulent, instead of voice/non-voiced. The results show that useful information is indeed present in turbulent or near unvoiced segments. A comparison with systems that use the entire signal or only the non-turbulent frames shows that the unvoiced or highly turbulent speech segments contain useful pathological information. (More)

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Paper citation in several formats:
Perdigão, F.; Neves, C. and Sá, L. (2012). PATHOLOGICAL VOICE DETECTION USING TURBULENT SPEECH SEGMENTS.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 238-243. DOI: 10.5220/0003775902380243

@conference{biosignals12,
author={Fernando Perdigão. and Cláudio Neves. and Luís Sá.},
title={PATHOLOGICAL VOICE DETECTION USING TURBULENT SPEECH SEGMENTS},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={238-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003775902380243},
isbn={978-989-8425-89-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - PATHOLOGICAL VOICE DETECTION USING TURBULENT SPEECH SEGMENTS
SN - 978-989-8425-89-8
AU - Perdigão, F.
AU - Neves, C.
AU - Sá, L.
PY - 2012
SP - 238
EP - 243
DO - 10.5220/0003775902380243

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