Automatic speech and singing classification in ambulatory recordings for normal and disordered voices

• DOI: 10.1121/1.5115804
• Publication Date: 2019-07-11T15:26:22Z
• Authors: Andrew J. Ortiz; Laura E. Toles; Katherine L. Marks; Silvia Capobianco; Daryush D. Mehta; Robert E. Hillman; Jarrad H. Van Stan
• Keywords: Adult; 03 medical and health sciences; Voice Disorders; 0302 clinical medicine; Voice; Humans; Monitoring, Ambulatory; Singing; Speech; Female; Vocal Cords; 0305 other medical science

Ambulatory voice monitoring is a promising tool for investigating phonotraumatic vocal hyperfunction (PVH), associated with the development of fold lesions. Since many patients PVH are professional vocalists, classifier was developed to better understand phonatory mechanisms during speech and singing. Twenty singers 20 matched healthy controls were monitored neck-surface accelerometer–based ambulatory monitor. An expert-labeled ground truth data set used train logistic regression on 15 subject-pairs fundamental frequency autocorrelation peak amplitude as input features. Overall classification accuracy 94.2% achieved held-out test set.