Automatic Acoustics Measurement of Audible Inspirations in Pathological Voices

Abstract

Audible inspiration is a type of speech perturbation used in conjunction with other acoustic observations to assess different types of pathologic conditions of speech associated with neurological or vocal cord disorders. The perception of this voice perturbation is very subjective and difficult to appraise in a consistent form across multiple utterances, subjects and disorders. This work reports an algorithm to model the perception of audible inspirations. It automatically segments the inspirations in continuous speech based on time-frequency characteristics and estimates the magnitude of the perturbation through a linear combination of the number, duration and the intensity of the inspirations. The algorithm was evaluated with the Massachusetts Eye and Ear Infirmary Voice database and two other databases containing recording from motor speech disorders. Results: a new method to automatically segment inspiratory phonation was developed. It provided an average segmentation accuracy of 84.4% and enabled accurate objective judgments of the perturbations associated with audible inspirations.