Voice analysis in adductor spasmodic dysphonia: Objective diagnosis and response to botulinum toxin

Suppa, Antonio; Asci, Francesco; Saggio, Giovanni; Marsili, Luca; Casali, Daniele; Zarezadeh, Zakarya; Ruoppolo, Giovanni; Berardelli, Alfredo; Costantini, Giovanni

doi:10.1016/j.parkreldis.2020.03.012

Cited by 35 publications

(51 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another important finding of our study concerns the comparable results achieved when examining voice samples collected during the emission of the vowel and the sentence [ 24 , 77 ]. This finding suggests the comparable ability of machine learning to recognize voice changes due to the combined effect of ageing and gender, during the sustained emission of a vowel as well as a sentence.…”

Section: Discussionmentioning

confidence: 89%

“…Future cohort studies comparing voice recordings in a larger number of samples of different ages (e.g., large samples of subjects in early, middle and late adulthood) will better examine whether age-related changes in voice can be considered biomarkers of human ageing. Furthermore, we believe that our study would provide new helpful information to clinicians to better distinguish physiologic ageing from pathological changes of the human voice in subjects affected by various speech disorders [ 77 , 84 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Asci

Costantini

Leo

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

show abstract

Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Asci

Costantini

Leo

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…including adductor-type spasmodic dysphonia. [14][15][16][17] In ET patients manifesting voice tremor, spectral analysis of voice recordings could be an appropriate methodological tool to assess the main frequency components of voice tremor. Indeed, although the frequency of upper limb tremor in ET typically ranges between 8 and 12 Hz, the frequency of voice tremor in patients manifesting this speech disorder remains unclear.…”

mentioning

confidence: 99%

Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor

et al. 2021

Self Cite

View full text Add to dashboard Cite

A BS TRACT: Background: Patients with essential tremor have upper limb postural and action tremor often associated with voice tremor. The objective of this study was to objectively examine voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor using voice analysis consisting of power spectral analysis and machine learning. Methods: We investigated 58 patients (24 men; mean age ± SD, 71.7 ± 9.2 years; range, 38-85 years) and 74 age-and sex-matched healthy subjects (20 men; mean age ± SD, 71.0 ± 12.4 years; range, 43-95 years). We recorded voice samples during sustained vowel emission using a high-definition audio recorder. Voice samples underwent sound signal analysis, including power spectral analysis and support vector machine classification. We compared voice recordings in patients with essential tremor who did and did not manifest clinically overt voice tremor and in patients who were and were not under the symptomatic effect of the best medical treatment.Results: Power spectral analysis demonstrated a prominent oscillatory activity peak at 2-6 Hz in patients who manifested a clinically overt voice tremor. Voice analysis with support vector machine classifier objectively discriminated with high accuracy between controls and patients who did and did not manifest clinically overt voice tremor and between patients who were and were not under the symptomatic effect of the best medical treatment. Conclusions: In patients with essential tremor, voice tremor is characterized by abnormal oscillatory activity at 2-6 Hz. Voice analysis, including power spectral analysis and support vector machine classification, objectively detected voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor.

show abstract

“…[3][4][5] 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. [3][4][5] Finally, the recording guidelines of Rusz et al 1 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.…”

mentioning

confidence: 99%

“…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. 4 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.…”

mentioning

confidence: 99%

Fostering Voice Objective Analysis in Patients with Movement Disorders

et al. 2021

Self Cite

View full text Add to dashboard Cite

Voice analysis in adductor spasmodic dysphonia: Objective diagnosis and response to botulinum toxin

Cited by 35 publications

References 25 publications

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor

Fostering Voice Objective Analysis in Patients with Movement Disorders

Contact Info

Product

Resources

About