Objective: To investigate differences in voice parameters between patients affected by adductor-type spasmodic dysphonia (ASD) and Healthy subjects (HS).

Background: Adductor-type spasmodic dysphonia (ASD) is a task-specific focal dystonia manifesting with involuntary laryngeal muscle spasms leading to intermittent strained/strangled voice. ASD is often poorly recognized by clinicians not familiar with the disorder, because of the lack of diagnostic criteria and of validated severity scales. In the present study, following our recently published observations, we performed voice analysis in ASD patients by using cepstral analysis. Cepstral analysis is based on Fourier transform of the logarithm power spectrum of an acoustic signal and reflects the dominant rahmonic in the voice sample. 

Methods: We investigate 20 ASD patients and 20 age and sex-matched healthy subjects (HS). Symptoms were scored using the Voice Handicap Index scale and a dysphonia clinical scale. Phoniatric evaluation included voice cepstral analysis. The crucial variable in the voice cepstral analysis is the normalized cepstral peak prominence (CPP). We collected voice samples using a high-definition audio recorder (H4n Zoom Corporation, Japan) and a Shure WH20 Dynamic Headset Microphone. Voice samples were digitized at 44.1 kHz, 24 bit, and analysed using the Matlab software. CPP together with other cepstral and spectral features, such as CPPS (smoothed CPP), Hi/Low frequencies rate, harmonics-to-noise ratio, shimmer and jitter were extracted. Finally, we performed a classification with both neural networks and Support Vector Machine (SVM), using Weka software.

Results: Voice analysis discriminates HS from ASD, with a sensitivity of 82% by using neural networks and 87% by applying SVM; and a specificity of 90% by using neural networks and 97% by applying SVM. Positive predictive value is 87% by using neural networks and 88% by applying SVM. Negative predictive value is 85% by using neural networks and 84% by applying SVM.

Conclusions: Cepstral analysis discriminates ASD patients from HS, representing a new helpful tool to better characterize voice abnormalities in ASD. These results suggest the idea that voice features extraction and classification are important instruments to support clinicians in the correct diagnosis of ASD, among different voice disorders. 

References:

1. Suppa A, Marsili L, Giovannelli F, Di Stasio F, Rocchi L, Upadhyay N, Ruoppolo G, Cincotta M, Berardelli A. Abnormal motor cortex excitability during linguistic tasks in adductor-type spasmodic dysphonia. Eur J Neurosci. 2015 Aug;42(4):2051-60. doi: 10.1111/ejn.12977.
2. Peterson, E.A., Roy, N., Awan, S.N., Merrill, R.M., Banks, R. & Tanner, K. (2013) Toward validation of the cepstral spectral index of dysphonia (CSID) as an objective treatment outcomes measure. J. Voice, 27, 401-410.

« Back to 2017 International Congress

MDS Abstracts - https://www.mdsabstracts.org/abstract/voice-cepstral-analysis-in-adductor-type-spasmodic-dysphonia/