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Authors: Farideh Jalalinajafabadi 1 ; Chaitaniya Gadepalli 2 ; Mohsen Ghasempour 1 ; Frances Ascott 2 ; Mikel Luján 1 ; Jarrod Homer 2 and Barry Cheetham 1

Affiliations: 1 University of Manchester, United Kingdom ; 2 Central Manchester University Hospitals Foundation Trust, United Kingdom

ISBN: 978-989-758-118-2

Keyword(s): GRBAS, Asthenia, MLR, KNNR.

Related Ontology Subjects/Areas/Topics: Audio and Video Quality Assessment ; Biomedical Applications ; Multimedia ; Multimedia Systems and Applications ; Telecommunications

Abstract: Vocal cord vibration is the source of voiced phonemes. Voice quality depends on the nature of this vibration. Vocal cords can be damaged by infection, neck or chest injury, tumours and more serious diseases such as laryngeal cancer. This kind of physical harm can cause loss of voice quality. Voice quality assessment is required from Speech and Language Therapists (SLTs). SLTs use a well-known subjective assessment approach which is called GRBAS. GRBAS is an acronym for a five dimensional scale of measurements of voice properties which were originally recommended by the Japanese Society of Logopeadics and Phoniatrics and the European Research for clinical and research use. The properties are ‘Grade’, ‘Roughness’, ‘Breathiness’, ‘Asthenia’ and ‘Strain’. The objective assessment of the G, R, B and S properties has been well researched and can be carried out by commercial measurement equipment. However, the assessment of Asthenia has been less extensively researched. This paper concerns t he objective assessment of ‘Asthenia’ using features extracted from 20 ms frames of sustained vowel /a/. We develop two regression prediction models to objectively estimate Asthenia against speech and language therapists (SLTs) scores. These regression models are ‘K nearest neighbor regression’ (KNNR) and ‘Multiple linear regression’(MLR). These new approaches for prediction of Asthenia are based on different subsets of features, different sets of data and different prediction models in comparison with previous approaches in the literature. The performance of the system has been evaluated using Normalised Root Mean Square Error (NRMSE) for each of 20 trials, taking as a reference the average score for each subject selected. The subsets of features that generate the lowest NRMSE are determined and used to evaluate the two regression models. The objective system was compared with the scoring of each individual SLT and was found to have a NRMSE, averaged over 20 trials, lower than two of them and only slightly higher than the third. (More)

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Paper citation in several formats:
Jalalinajafabadi F., Gadepalli C., Ghasempour M., Ascott F., Luján M., Homer J. and Cheetham B. (2015). Objective Assessment of Asthenia using Energy and Low-to-High Spectral Ratio.In Proceedings of the 12th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2015) ISBN 978-989-758-118-2, pages 76-83. DOI: 10.5220/0005545000760083

@conference{sigmap15,
author={Farideh Jalalinajafabadi and Chaitaniya Gadepalli and Mohsen Ghasempour and Frances Ascott and Mikel Luján and Jarrod Homer and Barry Cheetham},
title={Objective Assessment of Asthenia using Energy and Low-to-High Spectral Ratio},
booktitle={Proceedings of the 12th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2015)},
year={2015},
pages={76-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005545000760083},
isbn={978-989-758-118-2},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2015)
TI - Objective Assessment of Asthenia using Energy and Low-to-High Spectral Ratio
SN - 978-989-758-118-2
AU - Jalalinajafabadi F.
AU - Gadepalli C.
AU - Ghasempour M.
AU - Ascott F.
AU - Luján M.
AU - Homer J.
AU - Cheetham B.
PY - 2015
SP - 76
EP - 83
DO - 10.5220/0005545000760083

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