Automatic speaker independent dysarthric speech intelligibility assessment system

Tripathi, Ayush; Bhosale, Swapnil; Kopparapu, Sunil Kumar

doi:10.1016/j.csl.2021.101213

Cited by 9 publications

(7 citation statements)

References 56 publications

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“…In Some Cases, Studies Incorporate More Than One Technique. Following That, the Same Study Was Replicated, Thereby Increasing the Overall Number of Research Studies Method/Technique % Reference MFCCs/derived features from MFCCs 35.4% [ 13 , 15 , 55 , 70 , 72 , 80 , 87 , 94 , 95 , 97–99 , 101 , 112 , 117 , 127 , 131 , 133 , 134 ] Spectro-Temporal of utterances/keywords 26.15% [ 12 , 14 , 15 , 26 , 63 , 72 , 87 , 89 , 90 , 97 , 101–103 , 110 , 112 , 124 , 136 ] Articulation way/Speech timing 18.5% [ 13 , 27 , 58 , 81 , 86–88 , 111 , 113 , 124 , 129 ] Glottal flow 7.7% [ 58 , …”

Section: Discussionmentioning

confidence: 99%

“…Combining these two features shows more reliable results than others. [25][26][27] Notably applying ML techniques in speech recognition and augmented communication, enhancing accessibility and user experience, predictive and contextual communication, voice synthesis, and personalized language models. 25,28 ML models can learn and adapt from ML-powered ATS users.…”

Section: Introductionmentioning

confidence: 99%

“…Although the researchers found several ways and techniques for better detecting Dysarthria, analysis indicates that the combination of MFCC and Spectro Temporal utterance methods, such as, 87,94,124,138 achieved better accuracy than others. [13,15,55,70,72,80,87,94,95,[97][98][99]101,112,117,127,131,133,134] Spectro-Temporal of utterances/keywords 26.15% [12,14,15,26,63,72,87,89,90,97,[101][102][103]110,112,124,136] Articulation way/Speech timing 18.5% [13,27,58,81,[86][87][88]111,…”

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confidence: 99%

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Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

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Purpose Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. Methods The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. Originality This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014–2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. Findings The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

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“…The mode of meaning included the specific speech inputs into the proposed models. Studies that solely used speech features [15,[24][25][26][27][28][29][30][31][32][33][34][35] were heavily speaker-dependent on their approach and tended to lean more toward intelligibility of the dysarthric speaker than their comprehensibility.…”

Section: Mode Of Meaning Extraction Usedmentioning

confidence: 99%

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review

Alaka¹,

Kasamani²

2023

JMIR Rehabil Assist Technol

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Background Speech intelligibility and speech comprehension for dysarthric speech has attracted much attention recently. Dysarthria is characterized by irregularities in the speed, strength, pitch, breath control, range, steadiness, and accuracy of muscle movements required for articulatory aspects of speech production. Objective This study examined the contributions made by other studies involved in dysarthric speech comprehension. We focused on the modes of meaning extraction used in generalizing speaker-listener underpinnings in light of semantic ontology extraction as a desired technique, applied method types, speech representations used, and databases sourced from. Methods This study involved a systematic literature review using 7 electronic databases: Cochrane Database of Systematic Reviews, Web of Science Core Collection, Scopus, PubMed, ACM, IEEE Xplore, and Google Scholar. The main eligibility criterion was the extraction of meaning from dysarthric speech using natural language processing or understanding approaches to improve on dysarthric speech comprehension. In total, out of 834 search results, 30 studies that matched the eligibility requirements were acquired following screening by 2 independent reviewers, with a lack of consensus being resolved through joint discussion or consultation with a third party. In order to evaluate the studies’ methodological quality, the risk of bias assessment was based on the Cochrane risk-of-bias tool version 2 (RoB2) with 23 of the studies (77%) registering low risk of bias and 7 studies (33%) raising some concern over the risk of bias. The overall quality assessment of the study was done using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis). Results Following a review of 30 primary studies, this study revealed that the reviewed studies focused on natural language understanding or clinical approaches, with an increase in proposed solutions from 2020 onwards. Most studies relied on speaker-dependent speech features, while others used speech patterns, semantic knowledge, or hybrid approaches. The prevalent use of vector representation aligned with natural language understanding models, while Mel-frequency cepstral coefficient representation and no representation approaches were applied in neural networks. Hybrid representation studies aimed to reconstruct dysarthric speech or improve comprehension. Comprehensive databases, like TORGO and UA-Speech, were commonly used in combination with other curated databases, while primary data was preferred for specific or unique research objectives. Conclusions We found significant gaps in dysarthric speech comprehension characterized by the lack of inclusion of important listener or speech-independent features in the speech representations, mode of extraction, and data sources used. Further research is therefore proposed regarding the formulation of models that accommodate listener and speech-independent features through semantic ontologies that will be useful in the inclusion of key features of listener and speech-independent features for meaning extraction of dysarthric speech.

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“…A study has shown that over 47% of dysarthric sufferers reported often repeating what they say as healthy speakers find it difficult to understand them [5]. Speech and Language Pathologists (SLPs) are required to know the severity of dysarthria to assess the progression in the underlying cause of the impairment [6], which helps them to design effective treatment plans, exercises, and recovery sessions, as well as monitor the effectiveness of the therapy [7].…”

Section: Introductionmentioning

confidence: 99%

An Investigation to Identify Optimal Setup for Automated Assessment of Dysarthric Intelligibility using Deep Learning Technologies

2022

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Recent advances in deep learning have provided an opportunity to improve and automate dysarthria intelligibility assessment, offering a cost-effective, accessible, and less subjective way to assess dysarthric speakers. However, reviewing previous literature in the area determines that the generalization of results on new dysarthric patients was not measured properly or incomplete among the previous studies that yielded very high accuracies due to the gaps in the adopted evaluation methodologies. This is of particular importance as any practical and clinical application of intelligibility assessment approaches must reliably generalize on new patients; otherwise, the clinicians cannot accept the assessment results provided by the system deploying the approach. In this paper, after these gaps are explained, we report on our extensive investigation to propose a deep learning–based dysarthric intelligibility assessment optimal setup. Then, we explain different evaluation strategies that were applied to thoroughly verify how the optimal setup performs with new speakers and across different classes of speech intelligibility. Finally, a comparative study was conducted, benchmarking the performance of our proposed optimal setup against the state of the art by adopting similar strategies previous studies employed. Results indicate an average of 78.2% classification accuracy for unforeseen low intelligibility speakers, 40.6% for moderate intelligibility speakers, and 40.4% for high intelligibility speakers. Furthermore, we noticed a high variance of classification accuracies among individual speakers. Finally, our proposed optimal setup delivered an average of 97.19% classification accuracy when adopting a similar evaluation strategy used by the previous studies.

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Automatic speaker independent dysarthric speech intelligibility assessment system

Cited by 9 publications

References 56 publications

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review

An Investigation to Identify Optimal Setup for Automated Assessment of Dysarthric Intelligibility using Deep Learning Technologies

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