Discrimination of "hot potato voice" caused by upper airway obstruction utilizing a support vector machine.

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Discrimination of "hot potato voice" caused by upper airway obstruction utilizing a support vector machine.

Laryngoscope. 2018 Nov 28;:

Authors: Fujimura S, Kojima T, Okanoue Y, Shoji K, Inoue M, Hori R

Abstract
OBJECTIVES/HYPOTHESIS: "Hot potato voice" (HPV) is a thick, muffled voice caused by pharyngeal or laryngeal diseases characterized by severe upper airway obstruction, including acute epiglottitis and peritonsillitis. To develop a method for determining upper-airway emergency based on this important vocal feature, we investigated the acoustic characteristics of HPV using a physical, articulatory speech synthesis model. The results of the simulation were then applied to design a computerized recognition framework using a mel-frequency cepstral coefficient domain support vector machine (SVM).
STUDY DESIGN: Quasi-experimental research design.
METHODS: Changes in the voice spectral envelope caused by upper airway obstructions were analyzed using a hybrid time-frequency model of articulatory speech synthesis. We evaluated variations in the formant structure and thresholds of critical vocal tract area functions that triggered HPV. The SVMs were trained using a dataset of 2,200 synthetic voice samples generated by an articulatory synthesizer. Voice classification experiments on test datasets of real patient voices were then performed.
RESULTS: On phonation of the Japanese vowel /e/, the frequency of the second formant fell and coalesced with that of the first formant as the area function of the oropharynx decreased. Changes in higher-order formants varied according to constriction location. The highest accuracy afforded by the SVM classifier trained with synthetic data was 88.3%.
CONCLUSIONS: HPV caused by upper airway obstruction has a highly characteristic spectral envelope. Based on this distinctive voice feature, our SVM classifier, who was trained using synthetic data, was able to diagnose upper-airway obstructions with a high degree of accuracy.
LEVEL OF EVIDENCE: 2c Laryngoscope, 2018.

PMID: 30485441 [PubMed - as supplied by publisher]

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