Using speech rhythm knowledge to improve dysarthric speech recognition

Selouani, Sid‐Ahmed; Dahmani, Habiba; Amami, Rim; Hamam, Habib

doi:10.1007/s10772-011-9104-6

Cited by 18 publications

(5 citation statements)

References 15 publications

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“…Other binary algorithms examples are linear discriminant analysis (LDA), k-nearest neighbour but with lower accuracy (53). As is the case with other behavioural markers, adding sensitivity beyond binary outcomes (e.g., adding levels of intelligibility) can lead to decreases in accuracy (57). Some recent examples of hierarchical machine-learning model (combination of machine and deep learning algorithms) revealed promising results in ataxic groups (54,(58)(59)(60).…”

Section: Protocol Designmentioning

confidence: 99%

Quantitative speech assessment in ataxia- Consensus recommendations by the Ataxia Global Initiative Working Group on Digital-motor Biomarkers

Vogel

Grobe‐Einsler

Vasco

et al. 2023

Preprint

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Dysarthria is a common and debilitating symptom of many neurodegenerative diseases, including those resulting in ataxia. Changes to speech lead to significant reductions in quality of life, impacting the speaker in most daily activities. Recognition of its importance as an objective outcome measure in clinical trials for ataxia is growing. Its viability as an endpoint across the disease spectrum (i.e., pre-symptomatic onwards) means that trials can recruit ambulant individuals and later-stage individuals who are often excluded because of difficulty completing lower limb tasks. Here we discuss the key considerations for speech testing in clinical trials including hardware selection, suitability of tasks and their role in protocols for trials and propose a core set of tasks for speech testing in clinical trials. Test batteries could include forms suitable for remote short, sensitive, and easy to use, with norms available in several languages. The use of artificial intelligence also could improve accuracy and automaticity of analytical pipelines in clinic and trials.

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Section: Protocol Designmentioning

confidence: 99%

Quantitative speech assessment in ataxia- Consensus recommendations by the Ataxia Global Initiative Working Group on Digital-motor Biomarkers

Vogel

Grobe‐Einsler

Vasco

et al. 2023

Preprint

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“…Speech recognition uses a variety of modeling techniques to ensure better performance [15]. Rhythmic knowledgeof dysarthric speeches improves the system's accuracy [16] .…”

Section: Introductionmentioning

confidence: 99%

A Intelligent Speech Recognition Method Based on Stable Learning

Zhou,

Hu,

Wang

2024

Preprint

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Speech is the main way of human communication, which carries both the speaker's information and the speaker's emotion. A variety of applications can harness emotion in speech to serve human needs more effectively. The deep learning algorithm is a practical solution to the classification nature of speech recognition. Various algorithms have been widely utilized for voice data and achieved remarkable performance. However, in real life, the testing data to be under a different distribution from the training data, this will cause the out-of-distribution(OOD) problem. This article proposes a new domain generalization method for speech classification based on Stable Learning (StableNet) to address the OOD problem. The StableNet can remove the connection between features through learning weights for training samples, which makes deep models learn more useful features instead of the fake connection between the discriminative features and labels. We evaluate the performance of the proposed method by conducting speech classification experiments on voice datasets. We also investigate the importance of various features on speech classification in noisy environments. The effects of proposed method on speech recognition performance are evaluated.

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“…As is the case with other behavioural markers, adding sensitivity beyond binary outcomes (e.g. adding levels of intelligibility) can lead to decreases in accuracy [ 58 ]. Some recent examples of hierarchical machine-learning model (combination of machine and deep learning algorithms) revealed promising results in ataxic groups [ 55 , 59 – 61 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Speech Assessment in Ataxia—Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Markers

Vogel,

Sobanska,

Gupta

et al. 2023

Cerebellum

View full text Add to dashboard Cite

Dysarthria is a common and debilitating symptom of many neurodegenerative diseases, including those resulting in ataxia. Changes to speech lead to significant reductions in quality of life, impacting the speaker in most daily activities. Recognition of its importance as an objective outcome measure in clinical trials for ataxia is growing. Its viability as an endpoint across the disease spectrum (i.e. pre-symptomatic onwards) means that trials can recruit ambulant individuals and later-stage individuals who are often excluded because of difficulty completing lower limb tasks. Here we discuss the key considerations for speech testing in clinical trials including hardware selection, suitability of tasks and their role in protocols for trials and propose a core set of tasks for speech testing in clinical trials. Test batteries could include forms suitable for remote short, sensitive and easy to use, with norms available in several languages. The use of artificial intelligence also could improve accuracy and automaticity of analytical pipelines in clinic and trials.

show abstract

Using speech rhythm knowledge to improve dysarthric speech recognition

Cited by 18 publications

References 15 publications

Quantitative speech assessment in ataxia- Consensus recommendations by the Ataxia Global Initiative Working Group on Digital-motor Biomarkers

Quantitative speech assessment in ataxia- Consensus recommendations by the Ataxia Global Initiative Working Group on Digital-motor Biomarkers

A Intelligent Speech Recognition Method Based on Stable Learning

Quantitative Speech Assessment in Ataxia—Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Markers

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