A Systematic Review of Machine Learning based Automatic Speech Assessment System to Evaluate Speech Impairment

Jothi, K.; Mamatha, V L

doi:10.1109/iciss49785.2020.9315920

Cited by 4 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The unique characteristics of aphasic speech, such as longer pause times, reduced speech fluency, and distorted articulation, further complicate speech recognition [23]. These characteristics can vary widely among individuals with aphasia and may change over time, making it challenging to develop a one-size-fits-all solution.…”

Section: Speech Characteristicsmentioning

confidence: 99%

“…These characteristics can vary widely among individuals with aphasia and may change over time, making it challenging to develop a one-size-fits-all solution. Addressing these challenges requires the development of sophisticated algorithms capable of capturing and interpreting the nuanced features of aphasic speech accurately [16,23].…”

Section: Speech Characteristicsmentioning

confidence: 99%

“…This may involve incorporating mechanisms such as attention mechanisms or hierarchical modeling to capture and adapt to different linguistic characteristics. Studies in [20,21,23,31] focus on developing deep learning models tailored for handling the high variability in speech patterns among individuals with aphasia. By designing models specifically adapted to the unique characteristics of aphasic speech, such as paraphasic errors and neologisms, researchers aim to improve the accuracy and robustness of automatic speech recognition systems for aphasia assessment.…”

Section: Possible Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

Wang,

Cheng,

Sufi

et al. 2024

Computers

View full text Add to dashboard Cite

In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition’s complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 relevant documents, unveiling a landscape marked by significant advancements and persistent challenges. Through a methodological lens grounded in the PRISMA framework (Version 2020) and Machine Learning-driven tools like VosViewer (Version 1.6.20) and Litmaps (Free Version), the research delineates the high variability in speech patterns, the intricacies of speech recognition, and the hurdles posed by limited and diverse datasets as core obstacles. Innovative solutions such as specialized deep learning models, data augmentation strategies, and the pivotal role of interdisciplinary collaboration in dataset annotation emerge as vital contributions to this field. The analysis culminates in identifying theoretical and practical pathways for surmounting these barriers, highlighting the potential of deep learning technologies to revolutionize aphasia assessment and treatment. This review not only consolidates current knowledge but also charts a course for future research, emphasizing the need for comprehensive datasets, model optimization, and integration into clinical workflows to enhance patient care. Ultimately, this work underscores the transformative power of deep learning in advancing aphasia diagnosis, treatment, and support, heralding a new era of innovation and interdisciplinary collaboration in addressing this challenging disorder.

show abstract

Section: Speech Characteristicsmentioning

confidence: 99%

Section: Speech Characteristicsmentioning

confidence: 99%

Section: Possible Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

Wang,

Cheng,

Sufi

et al. 2024

Computers

View full text Add to dashboard Cite

show abstract

“…Although many works have been proposed in the literature, [11][12][13][14][15]71,72 to our knowledge, few reviews have explored using AI and ML in identifying, predicting, and assessing different speech disorders. For instance, 32 surveyed existing works on automatic assessment systems designed to evaluate patients' aphasia and the severity level of patients. In another study, 33 a review focused on the characteristics of dysarthric speech and introduced assistive solutions like robust automatic speech recognition (ASR) systems.…”

Section: Related Workmentioning

confidence: 99%

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

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

View full text Add to dashboard Cite

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).

show abstract

“…Spezifische Merkmale werden aus dem Signal extrahiert. Bei der Merkmalsextraktion als auch bei der Klassifikation werden Verfahren aus dem Bereich der digitalen Signalverarbeitung und Algorithmen des maschinellen Lernens genutzt [1].…”

Section: Technologien Zur Automatischen Sprech-und Sprachanalyseunclassified

Automatisierte Sprach- und Sprechanalyse in Forschung und Diagnostik bei Autismus-Spektrum-Störungen: Potenziale und Barrieren

Neitzel¹,

Tuschen²,

Ritterfeld³

2023

Sprache · Stimme · Gehör

View full text Add to dashboard Cite

Digitalisierungsprozesse versprechen auch in der Gesundheitsversorgung ein zeit- und kostensparendes Vorgehen. Eine besondere Engstelle stellt der umfassende Diagnostikprozess dar, welcher zur Identifizierung einer Autismus-Spektrum-Störung (ASS) nötig ist. Dieser Beitrag informiert über Potenziale und bestehende Barrieren in der Forschung und diagnostischen Anwendung automatisierter Sprachanalysen zur Identifikation von ASS bei Kindern.

show abstract

A Systematic Review of Machine Learning based Automatic Speech Assessment System to Evaluate Speech Impairment

Cited by 4 publications

References 45 publications

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

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

Automatisierte Sprach- und Sprechanalyse in Forschung und Diagnostik bei Autismus-Spektrum-Störungen: Potenziale und Barrieren

Contact Info

Product

Resources

About