Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition

Krishna, Gautam; Carnahan, Mason; Shamapant, Shilpa; Surendranath, Yashitha; Jain, Shuchi; Ghosh, Arundhati; Tran, Co; Millán, José del R.; Tewfik, A.H.

doi:10.1109/embc46164.2021.9629802

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Without sufficient data for training, deep learning models may struggle to generalize effectively across different types and severities of aphasia, leading to reduced performance and reliability in real-world applications [22,26]. Improving access to annotated datasets and developing techniques for efficient data annotation are essential for advancing research in this area [15,16,[27][28][29][30][31][32][33][34].…”

Section: Limited Annotated Datamentioning

confidence: 99%

“…Designing intuitive interfaces that provide interpretable and actionable insights derived from deep learning models can enhance their usability and acceptance among clinicians, ultimately improving patient care and outcomes. Integrating solutions seamlessly into clinical workflows ensures that deep learning-based tools are effectively incorporated into existing clinical practices [27]. By developing solutions that seamlessly integrate with clinical workflows and provide actionable insights for clinicians, researchers can enhance the utility and impact of deep learning technologies in aphasia assessment and treatment.…”

Section: Develop User-friendly Interfaces and Visualization Tools To ...mentioning

confidence: 99%

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

Wang,

Cheng,

Sufi

et al. 2024

Computers

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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.

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Section: Limited Annotated Datamentioning

confidence: 99%

Section: Develop User-friendly Interfaces and Visualization Tools To ...mentioning

confidence: 99%

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

Wang,

Cheng,

Sufi

et al. 2024

Computers

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Features of Internal Pronunciation of Words by a Group of People with Rhotacism in Comparison with Normative Pronunciation

Shevaldova,

Vartanov

2024

Studies in Computational Intelligence

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Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies

Herath

Weraniyagoda

Rajapaksha

et al. 2022

Sensors

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Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

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Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition

Cited by 4 publications

References 23 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

Features of Internal Pronunciation of Words by a Group of People with Rhotacism in Comparison with Normative Pronunciation

Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies

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