[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2018.11.020

Diagnosing Vocal Disorders using Cobweb Clustering of the Jitter, Shimmer, and Harmonics-to-Noise Ratio

Lee, Keonsoo (Medical Information Communication Technology, Soonchunhyang University)
Moon, Chanki (Department of Computer Science and Engineering Soonchunhyang University)
Nam, Yunyoung (Department of Computer Science and Engineering Soonchunhyang University)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.11, 2018 , pp. 5541-5554 More about this Journal

Abstract

A voice is one of the most significant non-verbal elements for communication. Disorders in vocal organs, or habitual muscular setting for articulatory cause vocal disorders. Therefore, by analyzing the vocal disorders, it is possible to predicate vocal diseases. In this paper, a method of predicting vocal disorders using the jitter, shimmer, and harmonics-to-noise ratio (HNR) extracted from vocal records is proposed. In order to extract jitter, shimmer, and HNR, one-second's voice signals are recorded in 44.1khz. In an experiment, 151 voice records are collected. The collected data set is clustered using cobweb clustering method. 21 classes with 12 leaves are resulted from the data set. According to the semantics of jitter, shimmer, and HNR, the class whose centroid has lowest jitter and shimmer, and highest HNR becomes the normal vocal group. The risk of vocal disorders can be predicted by measuring the distance and direction between the centroids.

Keywords

Cobweb; Clustering; HNR; Jitter; Shimmer; Vocal Disorder;

Citations & Related Records

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