The use of speech recognition technology by people living with amyotrophic lateral sclerosis: a scoping review

Cave, Richard; Bloch, Steven

doi:10.1080/17483107.2021.1974961

Cited by 17 publications

(4 citation statements)

References 43 publications

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“…Second, bulbar assessments were performed by traditional methods, such as symptom assessments and ALSFRS-R, Roads scores in our study. We expect future studies will use novel technologies such as automatic speech recognition technology for speech assessments ( 26 ) and oral manometry for swallowing assessments ( 27 ). Additionally, our study was conducted in a single center.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Slow and Forced Vital Capacity on Ability to Evaluate Respiratory Function in Bulbar-Involved Amyotrophic Lateral Sclerosis

Huang

Yang

et al. 2022

Front. Neurol.

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Background and ObjectiveThe percent-predicted forced vital capacity (FVC%) in the pulmonary function test (PFT) is generally used to evaluate the respiratory function in amyotrophic lateral sclerosis (ALS). The slow vital capacity (SVC) is another method to evaluate the respiratory function. Some neurologists found that the FVC% was not reflective of respiratory symptoms and the percent-predicted SVC (SVC%) was found to be higher in some patients with bulbar-onset ALS. We aimed to compare the percent predicted SVC (SVC%) with FVC% in evaluating the respiratory function and investigate the associations between the associations between clinical characteristics and the difference between the SVC% and the FVC% (SVC%-FVC%) in bulbar-involved ALS patients.MethodThis prospective study included patients with bulbar-involved ALS who visited the Peking University Third Hospital between October 2020 and November 2021. They underwent comprehensive clinical assessments, including bulbar symptom assessments, revised ALS functional rating scale (ALSFRS-R), Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (Roads), and PFTs. The group differences were analyzed using parametric and non-parametric tests.ResultsA total of 59 participants were initially enrolled, and 51 of them were included in the final analysis. In patients with bulbar-involved ALS, the SVC% (73.82 ± 21.95%) was significantly higher (p = 0.013) than the FVC% (71.42 ± 23.15%). After controlling for other relevant variables, a partial correlation analysis showed a significant correlation (r = −0.352, p = 0.041) between ALSFRS-R1 score and SVC%-FVC%.ConclusionOur prospective study found that the SVC% was significantly higher and more reflective of actual respiratory function than the FVC% in patients with bulbar-involved ALS. Furthermore, the severity of dysarthria was found to be positively correlated with SVC%-FVC%, providing a clinical marker for predicting SVC%-FVC%.

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Section: Discussionmentioning

confidence: 99%

Comparison of Slow and Forced Vital Capacity on Ability to Evaluate Respiratory Function in Bulbar-Involved Amyotrophic Lateral Sclerosis

Huang

Yang

et al. 2022

Front. Neurol.

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“…Eine Studie von Cave et al hat z. B. belegt, dass die Lebensqualität von Menschen mit ALS durch den Einsatz dieser Technologie gesteigert wird 8 . Es eröffnen sich möglicherweise Wege, Sprachstörungen zu kompensieren, indem den Nutzern eine freihändige Interaktion mit einer Vielzahl von Geräten ermöglicht wird.…”

Section: Ki-basierte Sprachanalyse Für Neurologische Erkrankungenunclassified

Einsatz KI-gestützter Diagnostik von Sprach- und Sprechstörungen bei neurodegenerativen Erkrankungen

Fischer,

Jäck

2023

Nervenheilkunde

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ZUSAMMENFASSUNG Gegenstand und Ziel Der Artikel behandelt die Anwendungsmöglichkeiten der KI-gestützten Sprachanalyse bei neurodegenerativen Erkrankungen. Das Ziel besteht darin, einen Überblick über die sprachlichen Auffälligkeiten bei verschiedenen Krankheiten zu geben und zu zeigen, wie KI-basierte Methoden zur Diagnosestellung und Behandlung eingesetzt werden können. Material und Methoden Es werden neurodegenerative Erkrankungen und ihre spezifischen sprachlichen Störungen vorgestellt. Die traditionellen Methoden der Sprachanalyse für neurologische Erkrankungen werden erläutert und Möglichkeiten der KI-gestützten Analyse diskutiert. Ergebnisse Die KI-basierte Sprachanalyse stellt eine vielversprechende Möglichkeit zur Früherkennung und Diagnosestellung von neurologischen Erkrankungen dar. Durch automatische Transkripte und Fehleranalysen können subtile Veränderungen der Sprache und des Sprechens erkannt und objektiviert werden. Die KI-basierte Sprachanalyse ermöglicht eine genaue und quantifizierbare Bewertung von sprachlichen Defiziten und kann Fachpersonal zusätzliche Informationen liefern. Schlussfolgerung Die KI-basierte Sprachanalyse bietet neue Möglichkeiten zur Früherkennung und Überwachung von neurologischen Erkrankungen. Sie kann subtile Veränderungen der Sprache frühzeitig erkennen und eine rechtzeitige Intervention ermöglichen. Dennoch sollte sie als unterstützendes Werkzeug betrachtet werden und nicht als Ersatz für die Expertise von Fachpersonal. Die KI-basierte Sprachanalyse kann Ressourcen schonen, die Genauigkeit der Diagnose verbessern und eine kontinuierliche Verlaufsdiagnostik ermöglichen. Klinische Relevanz Die KI-basierte Sprachanalyse kann dazu beitragen, neurodegenerative Erkrankungen frühzeitig zu erkennen und eine gezielte Behandlung einzuleiten. Sie bietet eine objektivierbare Methode zur Bewertung sprachlicher Defizite und kann die Diagnosestellung unterstützen.

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“…Numerous speech recognition-oriented techniques can also be used to help detect and asses speech impairment [50,51]. Gupta et al [52] suggested a residual network-based approach for detecting dysarthria severity level based on short speech segments, whereas Latha et al [53] employed deep learning and several acoustic cues to recognize dysarthric speech and generate discernible speech.…”

Section: Assessing Speech-signal Impairmentsmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

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The use of speech recognition technology by people living with amyotrophic lateral sclerosis: a scoping review

Cited by 17 publications

References 43 publications

Comparison of Slow and Forced Vital Capacity on Ability to Evaluate Respiratory Function in Bulbar-Involved Amyotrophic Lateral Sclerosis

Comparison of Slow and Forced Vital Capacity on Ability to Evaluate Respiratory Function in Bulbar-Involved Amyotrophic Lateral Sclerosis

Einsatz KI-gestützter Diagnostik von Sprach- und Sprechstörungen bei neurodegenerativen Erkrankungen

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

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