A Pattern Recognition Approach to Spasmodic Dysphonia and Muscle Tension Dysphonia Automatic Classification

GASTÓN SCHLOTTHAUER; MARÍA EUGENIA TORRES; MARÍA CRISTINA JACKSON MENALDI

JOURNAL OF VOICE : OFFICIAL JOURNAL OF THE VOICE FOUNDATION.

Elsevier

Año: 2010 vol. 24 p. 346 - 353

0892-1997

Spasmodic dysphonia (SD) and muscle tension dysphonia (MTD) are two voice disorders which present similar characteristics. Usually they can be differentiated only by experienced voice clinicians. There are many reasons that support the idea that SD is a neurological disease,requiring surgical treatments or, more usually, laryngeal botulinum toxin A injections as atherapeutic option. On the other hand, MTD is a functional disorder correctable with voice therapy.The importance of a correct diagnosis of these two disorders is critical at the treatment selectionmoment. In the present article, we present and compare the results of neural network and support vector machine based methods that can help the clinicians to confirm their diagnosis. As a preliminary approach to the problem we used only a sustained vowel /a/ to extract eight acoustic parameters. Then, a pattern recognition algorithm classifies the voice as normal, SD or MTD. For comparison purposes with previous works, we also separate the voices into normal and pathological (SD and MTD) with the methods here proposed. The results overcome the best reported classification between pathological and normal voices, and they have a good behavior for discrimination between MTD and SD.