Abstract
The acoustics of isolated vowels, e.g. of /a/, have in many studies been linked to pathological voice types, such as tracheoesophageal (TE) voice. To study the possibilities of objective and automatic classification of pathological TE voice types, the acoustic features of /a/ were quantified and subsequently classified using a suit of machine learning technologies. Best classification was achieved by using a voiced-voiceless measurement and the harmonics-to-noise ratio. Other common acoustic features were correlated to pathological type as well, but were less distinctive in classification. We conclude that for objective and automatic classification of TE voice pathology, voicing distinction and harmonics-to-noise ratio are most relevant. Index Terms: Tracheoesophageal speech, pathological speech, machine learning
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