Towards Objective Voice Assessment: The Diplophonia Diagram

• Published in: Journal of voice Vol. 31; no. 2; pp. 253.e17 - 253.e26
• Main Authors: Aichinger, Philipp, Roesner, Imme, Schneider-Stickler, Berit, Leonhard, Matthias, Denk-Linnert, Doris-Maria, Bigenzahn, Wolfgang, Fuchs, Anna Katharina, Hagmüller, Martin, Kubin, Gernot
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2017
• Subjects: Acoustic analysis; Acoustics; Algorithms; Detection; Diplophonia; Dysphonia; Humans; Laryngoscopy - methods; Logistic Models; Multivariate Analysis; Otolaryngology; Pattern Recognition, Automated; Phonation; Predictive Value of Tests; Prospective Studies; Reproducibility of Results; Severity of Illness Index; Signal processing; Signal Processing, Computer-Assisted; Sound Spectrography; Time Factors; Voice Disorders - diagnosis; Voice Disorders - physiopathology; Voice Quality; Signal processing; Dysphonia; Diplophonia; Detection; Acoustic analysis
• ISSN: 0892-1997; 1873-4588
• DOI: 10.1016/j.jvoice.2016.06.021

Summary Objectives Diplophonia is an often misinterpreted symptom of disordered voice, and needs objectification. An audio signal processing algorithm for the detection of diplophonia is proposed. Diplophonia is produced by two distinct oscillators, which yield a profound physiological interpretation. The algorithm's performance is compared with the clinical standard parameter degree of subharmonics (DSH). Study Design This is a prospective study. Methods A total of 50 dysphonic subjects with (28 with diplophonia and 22 without diplophonia) and 30 subjects with euphonia were included in the study. From each subject, up to five sustained phonations were recorded during rigid telescopic high-speed video laryngoscopy. A total of 185 phonations were split up into 285 analysis segments of homogeneous voice qualities. In accordance to the clinical group allocation, the considered segmental voice qualities were (1) diplophonic, (2) dysphonic without diplophonia, and (3) euphonic. The Diplophonia Diagram is a scatter plot that relates the one-oscillator synthesis quality ( S Q 1 ) to the two-oscillator synthesis quality ( S Q 2 ). Multinomial logistic regression is used to distinguish between diplophonic and nondiplophonic segments. Results Diplophonic segments can be well distinguished from nondiplophonic segments in the Diplophonia Diagram because two-oscillator synthesis is more appropriate for imitating diplophonic signals than one-oscillator synthesis. The detection of diplophonia using the Diplophonia Diagram clearly outperforms the DSH by means of positive likelihood ratios (56.8 versus 3.6). Conclusions The diagnostic accuracy of the newly proposed method for detecting diplophonia is superior to the DSH approach, which should be taken into account for future clinical and scientific work.