Fujimura Shintaro, Kojima Tsuyoshi, Okanoue Yusuke, Shoji Kazuhiko, Inoue Masato, Hori Ryusuke
Department of Otolaryngology , Tenri Hospital, Tenri, Nara, Japan.
Department of Electrical Engineering and Bioscience , School of Advanced Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan.
Laryngoscope. 2019 Jun;129(6):1301-1307. doi: 10.1002/lary.27584. Epub 2018 Nov 28.
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).
Quasi-experimental research design.
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.
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%.
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.
2c Laryngoscope, 129:1301-1307, 2019.
