Background: Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems. Summary: We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context. Key Messages: The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most cohorts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.

General Pathophysiology and Pathogenesis

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease that is characterized by progressive loss of the upper and lower motor neurons at the spinal or bulbar level. In ALS, the motor neurons degenerate or die and stop sending messages to the muscles. Unable to function, the muscles start to fasciculate, and waste away (atrophy). Eventually, the brain loses its ability to initiate and control voluntary movements [1, 2].

Social and Economic Impact

In 2013 the Centers for Disease Control and Prevention estimated that between 14,000 and 15,000 Americans have ALS [3]. In 2016 globally, 330,918 individuals had a motor neuron disease and its prevalence was higher in high sociodemographic index regions (especially in high-income North America and Western Europe). It is estimated that ALS has a worldwide median prevalence (proportion of a population with a disease) of 4.48 per 100,000 – 5.40 in Europe, 3.40 in the USA, and 2.34 in Asia [4]. The fact that ALS patients are looked after by their families causes physical, psychological, social, and economic problems for patients and their families. The economic impact is also relevant, standardized to the 2015 USD, the annual total cost per patient ranged from USD 13,667 in Denmark to as high as USD 69,475 in the USA, with the national economic burden of ALS estimated at USD 279–472 million in the USA [5]. These estimates do not account for costs such as mobility aids, medical expenses, and private personal support workers, not to mention indirect costs. Among others, age is an important factor for the appearance of the disease [6] and, as the population ages, the burden of motor neuron diseases, and ALS in particular, is likely to continue to grow in the next decades.

Classification

ALS can be classified in several different ways, but the most consensual division encompasses (a) upper motor neurons and (b) lower motor neurons. Upper motor neuron involvement leads to spasticity, weakness, and brisk deep tendon reflexes, while lower motor neuron manifestations include fasciculations, wasting, and weakness [7]. Individuals may develop problems with movement, swallowing (dysphagia), speaking or forming words (dysarthria), and breathing (dyspnea) [8]. Differences were found (a) between speakers with ALS and healthy controls and (b) between speakers with ALS and Parkinson disease, particularly in movement speed [9].

Diagnosis and Impact on Phonation Process

People with ALS demonstrate various speech symptoms at the time of diagnosis, ranging from normal speech to the use of augmentative communication. Particularly those with bulbar-onset ALS may have obvious speech deterioration before a definitive diagnosis [7].

ALS is mainly clinically diagnosed, and early uncertainty leads to a mean time from symptom onset to diagnosis of about 14 months [10]. Many diagnosis techniques are used: (a) electrodiagnostic tests such as electromyography and nerve conduction study (while nerve conduction studies in isolation cannot be used to make a diagnosis of anterior horn cell disease); (b) laboratory studies; and (c) neuroimaging, namely MRI of the brain and spinal cord, which is considered the most useful neuroimaging technique in ALS, mainly to exclude syndromes that mimic ALS [11].

Early diagnosis is important for enrollment in clinical trials and because medical care provided has been shown to prolong survival and improve quality of life for ALS patients [12, 13]. The most common drugs can slow the progression of the disease and reduce the decline in daily functioning (a combined strategy using edaravone and/or riluzole with suitable P-glycoprotein efflux-blocking drugs seems to lead to more favorable outcomes in ALS patients [14]). Therapy strategies can be more effective when the disease is diagnosed at an early stage of development. Objective bulbar assessment strategies are needed for improving early disease detection, monitoring disease progression, and optimizing the efficacy of therapeutic ALS drug therapy. Improved prediction accuracy of bulbar decline can be important for clinical care because decisions regarding communication intervention and palliative care are most effective when made early [15]. Early diagnosis is crucial for a better prognosis in the treatment of the disease and, to date, no other method offers such low cost and simplicity as voice-assisted diagnosis. Hence, evaluation of speech and speaking performance may be well suited for ALS early detection and monitoring [15-21]. In fact, in a large cohort, more than 33% of patients reported to have dysarthria problems [22]. The study of Caruso and Burton [23], back in 1987, is one of the first to address the topic of speech/voice parameters in ALS patients.

In this article, it is our objective to perform an exhaustive review of the use of voice and speech assessment for ALS diagnosis and monitoring of symptom evolution.

Our literature search was inspired by the PICO methodology [24] that implies the definition of patients/population, intervention, comparisons, and outcomes. We systematically searched PubMed and ScienceDirect for relevant articles published between January 2014 and September 2019 by applying boolean search operators with an iterative combination of search terms: A (“voice” OR “speech”) AND B (“Amyotrophic Lateral Sclerosis” OR “ALS disease”). This search was conducted in September 2019. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. For inclusion in this systematic review, an article had to address voice/speech analysis to assess/classify ALS disease condition. Our search strategy, as depicted in Figure 1, resulted in 1,287 unique articles being found. In a second phase, the titles/abstracts of the articles found were automatically analyzed and the presence of the keywords “automatic,” “classification,” “features,” “markers,” “acoustic,” “early” and “detection” was used as inclusion/rejection criteria. From these we have made a manual analysis of the titles/abstracts; the excluded articles were merely about voice symptoms in ALS and not about using voice analysis in ALS assessment. The remaining 7 articles and related references constitute the main object of this review [16-21, 25].

Fig. 1.

Development pipeline for the presented study.

Fig. 1.

Development pipeline for the presented study.

Close modal

In the study of Tsunoda et al. [17], the main objective was to study the importance of otorhinolaryngologic examinations, particularly those conducted by vocal specialists, that can make potentially important contributions to the diagnosis of bulbar-onset ALS patients. A total of 2,623 patients, who visited the ENT at the Voice Clinic, National Hospital Organization, Tokyo Medical Center from 2010 to 2017, were analyzed, with the primary complaint of speech or vocal dysfunction at the initial visit. Of the 2,623 patients, 8 patients visited the voice clinic after consultations with other physicians, but before receiving a diagnosis and who had initially been suspected of having bulbar-onset ALS due to slow, slurred speech. Those suspected ALS cases were analyzed in detail. In terms of results, every patient with suspected ALS consulted an average of 2.2 physicians before visiting the voice clinic and a total of 3.2 physicians before receiving the final diagnosis. The mean speech symptom duration before visiting the vocal clinic was 7.83 months in ALS and 24 months in multiple system atrophy patients. The time until final diagnosis after referrals to neurologists was 2.16 and 15.3 months, respectively. The authors concluded that otolaryngologists and primary care physicians should consider the possibility of ALS when patients present even with the only symptom of slow, slurred speech. They should then refer such patients to neurologists for definitive diagnoses, leading to early detection and treatment of ALS.

A new methodology was proposed in the work of Allison et al. [16] to determine the diagnostic utility of clinician speech ratings and patient self-report for detecting early bulbar changes associated with ALS, compared to instrumentation-based speech measures. The sample was composed of 36 individuals with ALS and 17 healthy control participants. Patients’ awareness of early bulbar motor involvement was assessed using self-reported scores on the ALS Functional Rating Scale-Revised (ALSFRS-R). Clinicians’ detection of early bulbar motor involvement was assessed through perceptual speech ratings by two experienced speech-language pathologists. Participants with ALS were grouped as “bulbar pre-symptomatic” or “bulbar symptomatic” based on self-report and clinician ratings, and compared to healthy controls on 6 instrumentation-based speech measures. ROC analysis was used to compare the sensitivity and specificity of perceptual and instrumentation-based measures for detecting bulbar changes in pre-symptomatic individuals. In terms of results, early bulbar changes that were documented using instrumentation-based measures were undetected by both patients and clinicians. ROC analyses indicated that instrumentation-based measures outperformed clinicians’ scaled severity ratings, and that percent pause time was the best measure for differentiating healthy controls from bulbar pre-symptomatic individuals with ALS. In conclusion, the findings suggested that instrumentation-based measures of speech could be helpful for early detection of bulbar changes due to ALS.

In the work of Chiaramonte et al. [18], the main objective was to study the role of different specialists in the diagnosis of ALS to understand changes in verbal expression and phonation, respiratory dynamics, and swallowing that occurred rapidly over a short period of time. This study consisted in analyzing 22 patients with bulbar ALS using several methods: voice assessment, ENT evaluation, multi-dimensional voice program, spectrogram, electroglottography, and fibertropic endoscopic evaluation of swallowing. In terms of results, otolaryngologists suspected the disease in 7 out of 22 patients affected by difficulty in speech articulation, clumsy tongue movement, and dysphagia to liquids. According to the results, in the early stage of bulbar ALS, the most common voice changes are pneumophonic coordination and the repetition of long sentences. The patients have difficulty with consecutive pronunciation of explosive and velar consonants and groups of vowels. On physical examination, deterioration of fine motor control of the tongue, sialorrhea, tongue fasciculations, and atrophy were present. Acoustic parameters such as jitter and shimmer, that evaluate the signal quality, were also evaluated.

As observed in Figure 2, jitter (absolute) is the cycle-to-cycle variation of fundamental frequency (f0 represents the main frequency for a given voice signal), i.e., the average absolute difference between consecutive periods, expressed as:

Fig. 2.

Time representation of a signal corresponding to the continuous phonation of a voiced sound. For jitter calculation consecutive periods (Ti) are considered, and for shimmer calculation, consecutive amplitude peaks (Ai) are considered.

Fig. 2.

Time representation of a signal corresponding to the continuous phonation of a voiced sound. For jitter calculation consecutive periods (Ti) are considered, and for shimmer calculation, consecutive amplitude peaks (Ai) are considered.

Close modal

and shimmer (relative) is defined as the average absolute difference between the amplitudes of consecutive periods, divided by the average amplitude, expressed as a percentage:

In the acoustic analysis of Chiaramonte et al. [18], jitter, shimmer, vf0 (coefficient of fundamental frequency variation, the relative standard deviation of f0), ATRI (amplitude tremor intensity index, the average ratio of the amplitude of the most intense low-frequency amplitude-modulating component to the total amplitude of the analyzed voice signal), FTRI (frequency tremor intensity index, the average ratio of the frequency magnitude of the most intensive low-frequency-modulating component to the total frequency magnitude of the analyzed voice signal), DVB (degree of voice breaks, the ratio of the total length of areas representing voice breaks to the time of the complete voice sample), DSH (degree of subharmonic components, the estimated relative evaluation of subharmonic to f0 components in the voice sample), and vAm (coefficient of amplitude variation, the relative standard deviation of the peak-to-peak amplitude) are increased, and NHR (noise-to-harmonics ratio, the average ratio of the inharmonic spectral energy in the frequency range 1,500–4,500 Hz to the harmonic spectral energy in the frequency range 70–4,500 Hz) and maximum phonation time (persistently pronounce the vowel “a” with a conscious and comfortable pitch and sound intensity for as long as possible) are reduced. In the spectrogram, there were new formants and more noise components. Electroglottography shows an aperiodic oscillation of the vocal folds, during the long glottal cycle. Once a month, patients underwent acoustic voice analysis, electroglottography and endoscopic evaluation to follow the disease progression. These results support the potential use of acoustic analysis and show the role of the otorhinolaryngologist in ALS diagnosis. A close collaboration with a neurologist allows to determine the severity and the progression of the disease.

The problem of evaluating abnormal voice qualities in patients with ALS was also tackled in the study of Tomik et al. [19]. For their study, 17 patients with ALS were evaluated using perceptual voice assessment videolaryngostroboscopy (VLS) including voice range and maximum phonation time (a clinical measurement of the longest time one can phonate a vowel, typically 25–35 for males and 15–25 for females), and objective acoustic voice analysis with IRIS software (including evaluation of jitter, shimmer, mean f0, and NHR (quantifies the relative amount of additive noise in relation to pure harmonic content). Examinations were performed three times at 6-month intervals. Pathological patterns were identified using the subjective GRBAS (grade, roughness, breathiness, asthenia, strain) [26] scale. There was no association between the type of voice abnormalities and the prognosis or progression of the disease. The VLS exam concluded that voice range was significantly smaller among male patients with ALS than in controls in the first examination. Other assessed voice parameters did not differ from those of controls. In women with ALS, acoustic analysis showed higher mean jitter in all three examinations in comparison with the control group. Mean shimmer was also higher in female patients in all examinations in comparison with controls. Mean NHR values were significantly higher in female patients in all examinations in comparison with controls.

To conclude, the examination of phonation disturbances is a useful method in ALS patients for the assessment of voice abnormalities and natural progression of the disease. Perceptual voice assessment is helpful in the detection of voice disturbance. VLS reveals abnormalities related mostly to the amplitude of vibrations and glottal closure. The aerodynamic assessment of phonation (maximum phonation time) was also useful in women for monitoring disease progression. Finally, acoustic voice analysis confirmed the occurrence of abnormalities. The analysis of voice qualities among patients with ALS allows the detection of various voice abnormalities associated with the natural progression of the disease. The acoustic analysis of voice of ALS patients with dysarthria was also explored by Xie et al. [25]. The voice recordings of 45 individuals (11 with diagnosed ALS), with the sustained phonation of the vowel /a/, were analyzed using the multi-dimensional voice program [27] and statistical differences (p < 0.05) were found between the ALS patients and the healthy control group. For the ALS patients, the NHR and jitter values were higher.

Besides the simple acoustic parameters, it is also possible to use phonation kinematics as complementary information. In Wang et al. [20], the article described a methodology for the automatic detection of ALS from speech acoustic and articulatory samples. This approach consisted in investigating the automatic detection of ALS from short, pre-symptom speech acoustic and articulatory samples using machine-learning approaches (support vector machine and deep neural network). Two electromagnetic articulographs were used for collecting speech acoustic and articulatory movement data. Adding articulatory motion information (from the tongue and lips) further improved the detection performance. In terms of results with the support vector machine when only acoustic data were used, the overall accuracy, specificity, and sensitivity were all above guessing level (50%). Adding lip data, from both the upper and lower lips, significantly increased the performance numbers of accuracy, specificity, and sensitivity, as well as when considering both lip data and tongue data, from both tongue tip and tongue body back. This study demonstrated the feasibility of automatic detection of ALS from pre-symptom intelligible speech samples. Experiments using a data set collected from 11 patients with ALS and 11 healthy talkers showed promising results. The experiments also demonstrated that adding articulatory information could improve the detection performance; in particular, adding lip information on top of acoustic data showed a significantly improved performance.

In the study of Rong et al. [21], the main objective was to determine the mechanisms of speech intelligibility disability due to neurologic impairments. Intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. A cohort composed of 66 individuals diagnosed with ALS provided the base information for the study. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed-effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. The intelligibility was modulated by a set of thoracic pressures corresponding to lip and fasting movements (articulatory), number of subtler lesions in the articulatory musculature, nasal (resonatory) flow, and maximum fundamental frequency (phonatory and speech). The model presented 95.6% intelligibility. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred.

In previous studies, it has been shown that the progression of ALS could imply the onset of dysarthrias that inevitably affect the communication process. In particular during speech articulation, reductions in the phonation rate as well as in the amplitude of the mandibular movements were identified [15]. Articulation quality measures, such as vowel space area and the formant centralization ratio, can be used, among others, as acoustic indicators of the vowel span range [28]. The reviewed studies complement the existing knowledge and reinforce the importance of voice and speech assessment as effective methods for ALS diagnosis and monitoring. These developments unfold in distinct, often contiguous, directions based on equally diverse approaches. The reduced overlap and the scientific space covered has widened the current knowledge. An overview of the most important acoustic features related to ALS is presented in Table 1.

Table 1.

Acoustic features related to ALS disease and size of the study population

Acoustic features related to ALS disease and size of the study population
Acoustic features related to ALS disease and size of the study population

Hence, as an extension to current knowledge and with the purpose of improving ALS detection and monitoring, we can highlight that Tsunoda et al. [17], using data from a hospital center, showed that patients with slow slurred speech who were evaluated by voice-specialized otolaryngologists and then referred to neurologists had a faster definitive ALS diagnosis than those who were not evaluated by these specialists.

Voice hoarseness, voice roughness, and increased jitter were noticed in ALS patients. Chiaramonte et al. [18] and Tomik et al. [19] both evaluated the abnormal voice qualities in patients with ALS via a multidisciplinary approach, and despite the distinct methods that were used in each, both demonstrated similar results. One of the most interesting aspects identified by Tomik et al. [19] was the fact that differences were detected between male and female results. In the VLS exam, while the voice range was significantly smaller among male patients with ALS, female patients showed decreased voice range with lowering of the maximum voice frequency.

Allison et al. [16] compared clinician speech ratings with instrumentation-based speech measures. Instrumentation-based speech measures outperformed two experienced speech-language pathologists, which suggests that speech changes can be detected earlier by utilizing instrumentation-based methods rather than voice assessment evaluated by voice specialists. This reinforces the importance of assisted voice-based diagnosis in the early detection of ALS since. In the findings and claims of Tsunoda et al. [17], it was shown that evaluation by a voice specialist could contribute to identifying patterns of the disease.

As future directions, we mentioned that the feasibility of automatic detection of ALS from pre-symptomatic intelligible speech samples was reported by Wang et al. [20]. Despite the limited number of patients [11], the approach allowed distinguishing healthy individuals from ALS patients without human intervention. With a more exhaustive development, the used methodology could form the basis for a computational algorithm for the automatic detection of ALS from speech acoustic and articulatory samples.

The reduced velocities of lip and jaw movements and resonatory impairments served as predictors of the decline of speech intelligibility in ALS. These conclusions by Rong et al. [21], plus the improvement of methods that register the articulatory motion information from lips and tongue, and furthermore the addition of more data obtained via instrumentation-based measurements may result in the perfect ALS classification/diagnosis system. The creation of an algorithm that can process a combined and optimized feature set comprising the abovementioned data and automatically detect/classify ALS disease may be the future, allowing early detection and frequent monitoring.

Concerning the acoustic features, it is important to note that increased jitter and shimmer were commonly (3 out of the 6 authors) detected and related to the evolution of the disease, and that two authors detected and established a relationship between vf0, roughness and breathiness of the voice and the presence of the ALS disease. There were two articles that related ALS disease with a decline in speech intelligibility.

Although the cohort that supported the work of Tomik et al. [19] was relatively small, it is important to enhance the gender difference that was identified, since this could be of extreme importance in improving the process of early ALS detection through voice.

Promising results were found for the detection of ALS from speech samples and that this can be a feasible process even in pre-symptomatic, intelligible speech conditions (RQ1) (Fig. 1). Many parameters can be used and combined to detect and classify ALS patterns: acoustic parameters, such as shimmer, jitter, or vowel space area, as well as prosodic parameters such as intonation or duration have been reported to provide good results (RQ2). However, there is still no established set of parameters that can be used as a reference, allowing one to compare results or to establish baselines. Awareness regarding ALS could result in more studies being conducted in voice analysis and a faster evolution leading to an early detection/classification of ALS disease (RQ3). Voice-based ALS classification could be a useful methodology, but further developments must still be made to make it a standard practice. In particular:

1.It is paramount to continue to invest in data collection since the amount of acoustic data is still limited and not often with open access. Strategies like crowdsourcing and gamification can be used to engage ALS patients in data collection campaigns for clinical trials, disseminate technology, and obtain direct feedback from potential users.

2.Work must be carried out towards the definition of specific bio-acoustic markers that allow differentiating ALS from other neurodegenerative diseases. Abnormal values for parameters such as jitter, shimmer, or NHR are enough to achieve a definitive diagnosis. Software packages like OpenSmile [29] allow obtaining an extended set of parameters that still lack exploration for the purpose of detecting/classifying ALS.

3.There is no established standard for the calculation of parameters such as jitter and shimmer – often reported to have altered values in ALS patients. The software packages used by researchers have different algorithms to estimate the f0 and these have evolved over the years. Hence, results obtained with different applications or different versions can provide distinct values in the presence of similar signals. Additionally, the algorithms for f0 estimation have been developed for healthy voices and their performance is not always robust to establish pathology thresholds or ranges based on the reported results to date.

The authors have no conflicts of interest to declare.

This work was partially funded by the Portuguese Fundação para a Ciência e Tecnologia (grant No. FCT UIDB/04730/2020).

H.V., N.C., and T.S. were mainly involved in writing the manuscript. S.R. and L.C. were mainly involved in designing and revising the study. All authors contributed to the writing and approved the final version of the manuscript.

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