Acoustic and Physiological Voice Assessment And Maximum Phonation Time In Patients With Different Types Of Dysarthria

Portalete, Caroline Rodrigues; Moraes, Denis Altieri de Oliveira; Pagliarin, Karina Carlesso; Keske‐Soares, Márcia; Cielo, Carla Aparecida

doi:10.1016/j.jvoice.2021.09.034

Cited by 3 publications

(1 citation statement)

References 64 publications

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“…Consequently, AVQI serves as an excellent diagnostic method for obtaining scores from the dysphonic population and should be investigated in other voice issues. According to Portalete's results [31], while the characteristics detected in the evaluations were similar to those expected from individuals with dysarthria, it is difficult to establish a differential diagnosis of this disorder based solely on auditory and physiological criteria. Similarly, the team of Barsties et al [32] analyzed two acoustic properties, the cepstral spectral index of dysphonia (CSID) and AVQI, which have gained popularity as valid and reliable multiparametric indicators in the objective evaluation of hoarseness due to their inclusion of continuous speech and sustained vowels.…”

Section: State Of the Art Reviewmentioning

confidence: 86%

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

et al. 2023

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Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.

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Section: State Of the Art Reviewmentioning

confidence: 86%

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

et al. 2023

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Automatic speech-based assessment to discriminate Parkinson’s disease from essential tremor with a cross-language approach

Rios-Urrego,

Rusz,

Orozco-Arroyave

2024

npj Digit. Med.

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Parkinson’s disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns—hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET—the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models’ performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

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Speech and Nonspeech Parameters in the Clinical Assessment of Dysarthria: A Dimensional Analysis

et al. 2023

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Nonspeech (or paraspeech) parameters are widely used in clinical assessment of speech impairment in persons with dysarthria (PWD). Virtually every standard clinical instrument used in dysarthria diagnostics includes nonspeech parameters, often in considerable numbers. While theoretical considerations have challenged the validity of these measures as markers of speech impairment, only a few studies have directly examined their relationship to speech parameters on a broader scale. This study was designed to investigate how nonspeech parameters commonly used in clinical dysarthria assessment relate to speech characteristics of dysarthria in individuals with movement disorders. Maximum syllable repetition rates, accuracies, and rates of isolated and repetitive nonspeech oral–facial movements and maximum phonation times were compared with auditory–perceptual and acoustic speech parameters. Overall, 23 diagnostic parameters were assessed in a sample of 130 patients with movement disorders of six etiologies. Each variable was standardized for its distribution and for age and sex effects in 130 neurotypical speakers. Exploratory Graph Analysis (EGA) and Confirmatory Factor Analysis (CFA) were used to examine the factor structure underlying the diagnostic parameters. In the first analysis, we tested the hypothesis that nonspeech parameters combine with speech parameters within diagnostic dimensions representing domain–general motor control principles. In a second analysis, we tested the more specific hypotheses that diagnostic parameters split along effector (lip vs. tongue) or functional (speed vs. accuracy) rather than task boundaries. Our findings contradict the view that nonspeech parameters currently used in dysarthria diagnostics are congruent with diagnostic measures of speech characteristics in PWD.

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Acoustic and Physiological Voice Assessment And Maximum Phonation Time In Patients With Different Types Of Dysarthria

Cited by 3 publications

References 64 publications

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

Automatic speech-based assessment to discriminate Parkinson’s disease from essential tremor with a cross-language approach

Speech and Nonspeech Parameters in the Clinical Assessment of Dysarthria: A Dimensional Analysis

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