Fostering Voice Objective Analysis in Patients with Movement Disorders

We read with great interest the review article recently published in Movement Disorders entitled “Guidelines for Speech Recording and Acoustic Analyses in Dysarthrias of Movement Disorders” by Rusz and colleagues. The issue addressed by this article is relevant and provides significant advances in the objective evaluation of voice impairment in patients with movement disorders. Historically, the first pioneering attempt to measure and characterize voice impairment objectively, in patients with neurologic disorders, has been achieved by using the qualitative analysis of voice audio recordings. Those seminal studies have promoted great research interest in the field over the subsequent decades. Indeed, growing technological advances in the 80s have progressively improved the quality of audio recordings mainly because of the shift from analogic to digital voice analysis. More recently, automatic voice analysis has allowed the objective extraction of several features, including those related to energy (eg, root-mean-square energy), spectrum (eg, fundamental frequency, spectral harmonicity, spectral slope, and harmonic-to-noise ratio), and waveform (eg, shimmer and jitter) of the recorded voice. By reviewing the currently available procedures and suggesting guidelines of voice recordings in patients with specific hypokinetic and hyperkinetic movement disorders, the manuscript of Rusz and colleagues should be considered as a reference article promoting advances in the field. We believe that Rusz and colleagues have not discussed some methodological aspects concerning the objective assessment of voice in healthy subjects and patients with movement disorders. The experimental approach proposed by the authors includes voice evaluation through spectral analysis only. Although the spectral analysis has proved to be effective in classifying voice impairment in movement disorders, alternative and more innovative techniques, including machine learning, could also have been discussed. Human voice is a complex biological phenomenon consisting of highdimensional data. Accordingly, due to the ability to dynamically combine large data sets of features, machine learning may be considered a more appropriate tool than spectral analysis to automatically classify voice abnormalities. Indeed, by applying algorithms of machine learning, we have recently classified and measured voice abnormalities as well as the response to pharmacologic treatments, objectively and with higher sensitivity/specificity than spectral analysis, in patients with spasmodic dysphonia. A further comment concerns the specific speech task for voice analysis. The authors’ guidelines imply the acquisition of sustained vowel emissions, sequential and alternating motion rates, reading passage, and monologue. However, recent data from our group show similar results when comparing the sustained emission of vowels and sentences (eg, connected speech). We therefore suggest that the sustained emission of a vowel could be the most appropriate technical solutions for preventing linguistic confounding and achieving standardized worldwide procedures. Finally, the recording guidelines of Rusz et al require voice samples collected only in a specialized laboratory using dedicated hi-tech audio recorders under expert supervision. We have recently proposed an alternative solution consisting of voice recordings acquired by currently available smartphones that can simplify the experimental procedures in a more ecologic scenario and provide the background for future application in the context of telemedicine.