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On the Use of Voice Signals for Studying Sclerosis Disease computers Article On the Use of Voice Signals for Studying Sclerosis Disease Patrizia Vizza 1,*, Giuseppe Tradigo 2, Domenico Mirarchi 1,* ID , Roberto Bruno Bossio 3 and Pierangelo Veltri 1,* ID 1 Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy 2 Department of Computer Engineering, Modelling, Electronics and Systems (DIMES), University of Calabria, 87036 Rende, Italy; [email protected] 3 Neurological Operative Unit, Center of Multiple Sclerosis, Provincial Health Authority of Cosenza, 87100 Cosenza, Italy; [email protected] * Correspondence: [email protected] (P.V.); [email protected] (D.M.); [email protected] (P.V.) Received: 16 October 2017; Accepted: 23 November 2017; Published: 28 November 2017 Abstract: Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient’s life quality. Keywords: multiple sclerosis; vocal signal analysis; vowel metric; acoustic analysis 1. Introduction Neurodegenerative diseases concern the central nervous system and are characterized by chronic and selective neuron cell death. Neuronal deterioration may result in cognitive deficits, dementia, motor changes, and behavioral and psychological disorders. The neurodegenerative diseases are conditions primarily affecting neurons and causing the disruption of information flow within the brain and between the brain and rest of the body [1]. Multiple sclerosis (MS) is one of the most common neurodegenerative disorders. It is a chronic demyelinating disease that affects the central nervous system, interfering with nerve impulses within the brain, the spinal cord and the optic nerves [2]. Although the evolution in time of MS is different for each patient, four main types of MS can be identified: (i) clinically isolated syndrome (CIS); (ii) primary progressive MS (PPMS); (iii) relapsing–remitting MS (RRMS); and (iv) secondary progressive MS (SPMS) [3]—the last two being the most common. In the initial phase, 85% of the subjects affected by MS are diagnosed with RRMS. In these subjects, a specific behavior is observed: an increase in disability produced by clearly defined attacks or neurological symptoms, alternating with recovery phases, which worsen until total inactivity. The 30–50% of subjects affected by RRMS develop SPMS within 10 years. SPMS is characterized by a progressive worsening of the neurological function and a consequent increase in disability over time. Computers 2017, 6, 30; doi:10.3390/computers6040030 www.mdpi.com/journal/computers Computers 2017, 6, 30 2 of 12 MS presents specific symptomatologies, among which is speech impairment, also known as dysarthria [4], a motor speech disorder of neurological origin resulting from neurological injury due to damages in the central or peripheral nervous system. Dysarthria is the condition of a faulty articulation of sounds or words. In dysarthria, language functions are normal and the patient speaks with proper syntax, but pronunciation is defective because of problems with muscular movements needed for speech production [5]. Dysarthria may be more or less severe until it reaches a complete deficit. Dysarthria is an unfavorable symptom present not only in MS but also in other neurological diseases, for example, in amyotrophic lateral sclerosis as well as in Parkinson’s disease [6,7]. Therefore, the evaluation of dysarthria represents a valid clinical support to otolaryngologists, neurologists and speech pathologists for early and differential diagnosis and for documenting disease progression. Generally, this evaluation is performed by using a noninvasive speech signal analysis [8]. Acoustic analysis has been reported in literature as a useful tool to evaluate and characterize pathological vocal signals [9–11] and to show statistically significant differences with respect to normal subjects [12,13]. In the last few years, traditional and new acoustic measures of dysarthric speech have been proposed as alternative methods to differentiate dysarthric from healthy speech [14,15]. The authors in [16] analyzed acoustic parameters as well as fundamental frequency (F0), jitter, shimmer and Harmonics-to-Noise-Ratio (HNR) measures to define a procedure for the automatic diagnosis of larynx pathologies. In [17], a set of vowel metrics are presented, as derived from spectral and temporal measurements of vowel tokens embedded in phrase production, used to distinguish healthy from dysarthric speech. Several signal processing algorithms have been implemented to perform vocal signal analysis with the aim of identifying pathological voices [18,19]. The contribution in [20] compares the results of voice self-assessment performed by patients with MS with the results of expert perceptual assessments made by vocal therapists. Vocal difficulties reported by patients in terms of the voice handicap index (VHI) have been confirmed and rated by specialists using the Grade, Roughness, Breathiness, Asthenia, Strain scale (GRBAS). In [21], a study was conducted to explore temporal, spectral and phonatory acoustic features of MS patients compared to control subjects. It was found that the dysarthric symptoms of MS patients largely mirror their different underlying neuromotor dysfunctions and that they exhibit temporal and articulatory deviation when compared to the control subjects. In [22], the authors investigated the presence of dysphonic symptoms in MS patients, highlighting a gender difference in terms of fundamental frequency deviation, noise, and jitter: women show fewer voice variations than men. Moreover, software tools have been produced to extract vocal features for signal analysis: the Multi-Dimensional Voice Program (MDVP) and PRAAT (it is a software for the scientific analysis of speech in phonetics) are the most common tools used for voice analysis in clinical practice [23,24]. A comparison between these tools has been proposed in [25] with regard to the evaluation of jitter measurements. To better define the results of acoustic analysis and to furnish additional information, the vowel metric is often used to integrate voice signal evaluation [22,26,27]. For example, the contribution proposed in [28] regards the definition of speech parameters, such as the vowel space area (VSA) and the formant centralization ratio (FCR). Additionally, sharing data and information for increasing knowledge can be performed in a distributed laboratory, as in [29–31]. Voice signal analysis and the vowel metric help to show significant differences between patients with MS and normal subjects [32,33], justifying the assumption that speech analysis may become a helpful tool for the diagnosis and monitoring of disease progression. In this paper, the evaluation of vocal signals in patients with MS has been performed by using the methodologies of both acoustic analysis and the vowel metric. In particular, the PRAAT tool has been used to perform the acoustic analysis and an algorithm written in Matlab has been used to calculate the vowel metrics. Lastly, statistical analyses have been used to evaluate the results. The paper is composed as follows: the next paragraph reports a brief description of the phonetic apparatus. Section2 describes the methodologies used for processing the vocal signals, and Section3 explains the results of the analyses. Section4 concludes the paper. Computers 2017, 6, 30 3 of 12 Phonetic Apparatus The phonetic apparatus is responsible for human voice production. This apparatus is extremely sophisticated and is composed of organs that may be mobile or fixed. The mobile organs are the lips, jaw, tongue, and vocal cords; the fixed organs are the teeth, alveoli, palate and soft palatine veil. Other organs such as the lungs, larynx, pharynx and nose are responsible for the flow of air [34]. An illustration of the phonetic apparatus is depicted in Figure1[ 35]. The main organs and their functions are the following: • Lungs—these produce the airflow that enters into the bronchus and trachea. • Larynx—this contains the vocal cords that vibrate to produce vowels. Vocal cords are folds of muscles located at the level of the glottis, which represents the space between the vocal cords. The vocal cords vibrate when they are closed to obstruct the airflow through the glottis. • Oral cavity—this is composed by the tongue, palate, lips, and teeth. The tongue allows the articulation of consonants
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