Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Classification of Dysarthria based on the Levels of Severity. A Systematic Review (2310.07264v1)

Published 11 Oct 2023 in cs.LG

Abstract: Dysarthria is a neurological speech disorder that can significantly impact affected individuals' communication abilities and overall quality of life. The accurate and objective classification of dysarthria and the determination of its severity are crucial for effective therapeutic intervention. While traditional assessments by speech-language pathologists (SLPs) are common, they are often subjective, time-consuming, and can vary between practitioners. Emerging machine learning-based models have shown the potential to provide a more objective dysarthria assessment, enhancing diagnostic accuracy and reliability. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels. Specifically, this review will focus on determining the most effective set and type of features that can be used for automatic patient classification and evaluating the best AI techniques for this purpose. We will systematically review the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. Data extraction will include methodologies used, the type of features extracted for classification, and AI techniques employed. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (101)
  1. Estimates of the prevalence of speech and motor speech disorders in persons with complex neurodevelopmental disorders, Clinical Linguistics & Phonetics 33 (2019) 707–736.
  2. R. D. Kent, J. C. Rosenbek, Acoustic patterns of apraxia of speech, Journal of Speech, Language, and Hearing Research 26 (1983) 231–249.
  3. Sensitivity and specificity of an acoustic-and perceptual-based tool for assessing motor speech disorders in french: The monpage-screening protocol, Clinical Linguistics & Phonetics 35 (2021) 1060–1075.
  4. Breathiness indices for classification of dysarthria based on type and speech intelligibility, in: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), IEEE, 2019, pp. 266–270.
  5. What speech can tell us: A systematic review of dysarthria characteristics in multiple sclerosis, Autoimmunity reviews 17 (2018) 1202–1209.
  6. A scoping review of interventions for adults with dysarthria following traumatic brain injury, Brain injury 34 (2020) 466–479.
  7. C. Mackenzie, Dysarthria in stroke: a narrative review of its description and the outcome of intervention, International journal of speech-language pathology 13 (2011) 125–136.
  8. Assessment of dysarthria and dysphagia in patients with amyotrophic lateral sclerosis: Review of the current evidence, Muscle & Nerve 64 (2021) 520–531.
  9. Speech pathology management of non-progressive dysarthria: a systematic review of the literature, Disability and Rehabilitation 42 (2020) 296–306.
  10. A systematic review of group intervention for acquired dysarthria in adults, Disability and Rehabilitation 44 (2022) 3002–3018.
  11. Y.-J. Park, J.-M. Lee, Effect of acupuncture intervention and manipulation types on poststroke dysarthria: a systematic review and meta-analysis, Evidence-Based Complementary and Alternative Medicine 2020 (2020).
  12. A. K. Shanmugam, R. Marimuthu, A critical analysis and review of assistive technology: advancements, laws, and impact on improving the rehabilitation of dysarthric patients, Handbook of Decision Support Systems for Neurological Disorders (2021) 263–281.
  13. Characterizing dysarthria diversity for automatic speech recognition: A tutorial from the clinical perspective, Frontiers in Computer Science (2022) 43.
  14. R. Chiaramonte, M. Vecchio, A systematic review of measures of dysarthria severity in stroke patients, Pm&r 13 (2021) 314–324.
  15. R. Palmer, P. Enderby, Methods of speech therapy treatment for stable dysarthria: A review, Advances in Speech Language Pathology 9 (2007) 140–153.
  16. A review of automated intelligibility assessment for dysarthric speakers, in: 2021 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), IEEE, 2021, pp. 19–24.
  17. “you say severe, i say mild”: Toward an empirical classification of dysarthria severity, Journal of Speech, Language, and Hearing Research 64 (2021) 4718–4735.
  18. Apraxia of speech theory, assessment, differential diagnosis, and treatment: Past, present, and future, Speech motor control in normal and disordered speech: Future developments in theory and methodology (2017) 195–221.
  19. J. M. Barkmeier-Kraemer, H. M. Clark, Speech–language pathology evaluation and management of hyperkinetic disorders affecting speech and swallowing function, Tremor and Other Hyperkinetic Movements 7 (2017).
  20. P. Enderby, Frenchay dysarthria assessment, British Journal of Disorders of Communication 15 (1980) 165–173.
  21. The voice handicap index (vhi) development and validation, American journal of speech-language pathology 6 (1997) 66–70.
  22. S. Blaustein, A. Bar, Reliability of perceptual voice assessment, Journal of communication disorders 16 (1983) 157–161.
  23. S. A. S. Al Yaari, N. Almaflehi, Validation of communication activities of daily living-(cadl-2) on arab aphasics: Controlled study, J Study Engl Linguist 2 (2014) 34.
  24. A report of assessment tools for individuals with dysarthria, The Open Public Health Journal 12 (2019).
  25. J. I. Godino-Llorente, P. Gomez-Vilda, Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors, IEEE Transactions on Biomedical Engineering 51 (2004) 380–384.
  26. Prosody-based measures for automatic severity assessment of dysarthric speech, Applied Sciences 10 (2020) 6999.
  27. A. Gallardo-Antolín, J. M. Montero, An auditory saliency pooling-based lstm model for speech intelligibility classification, Symmetry 13 (2021) 1728.
  28. A multitask learning approach to assess the dysarthria severity in patients with parkinson’s disease., in: INTERSPEECH, 2018, pp. 456–460.
  29. Feature-driven machine learning to improve early diagnosis of parkinson’s disease, Expert Systems with Applications 110 (2018) 182–190.
  30. Dysarthria detection based on a deep learning model with a clinically-interpretable layer, JASA Express Letters 3 (2023) 015201.
  31. Acoustic studies of dysarthric speech: Methods, progress, and potential, Journal of communication disorders 32 (1999) 141–186.
  32. Formant centralization ratio: A proposal for a new acoustic measure of dysarthric speech (2010).
  33. Vowel contrast and speech intelligibility in dysarthria, Folia Phoniatrica et Logopaedica 63 (2011) 187–194.
  34. New spanish speech corpus database for the analysis of people suffering from parkinson’s disease., in: LREC, 2014, pp. 342–347.
  35. Artificial intelligence for dysarthria assessment in children with ataxia: A hierarchical approach, IEEE Access 9 (2021) 166720–166735.
  36. D. Kempler, D. Van Lancker, Effect of speech task on intelligibility in dysarthria: A case study of parkinson’s disease, Brain and language 80 (2002) 449–464.
  37. Automatic speaker recognition using mel-frequency cepstral coefficients through machine learning, CMC-Computers Materials & Continua 71 (2022).
  38. A. A. Joshy, R. Rajan, Dysarthria severity classification using multi-head attention and multi-task learning, Speech Communication 147 (2023) 1–11.
  39. Automatic detection of amyotrophic lateral sclerosis (als) from video-based analysis of facial movements: speech and non-speech tasks, in: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), IEEE, 2018, pp. 150–157.
  40. Kinematic analysis of lower lip movements in ataxic dysarthria, Journal of Speech, Language, and Hearing Research 38 (1995) 1252–1259.
  41. Assessing speech intelligibility of pathological speech in sentences and word lists: The contribution of phoneme-level measures, Journal of Communication Disorders 102 (2023a) 106301.
  42. Assessing speech intelligibility of pathological speech: test types, ratings and transcription measures, Clinical Linguistics & Phonetics 37 (2023b) 52–76.
  43. K. Lehner, W. Ziegler, The impact of lexical and articulatory factors in the automatic selection of test materials for a web-based assessment of intelligibility in dysarthria, Journal of Speech, Language, and Hearing Research 64 (2021) 2196–2212.
  44. D. M. Powers, Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation, arXiv preprint arXiv:2010.16061 (2020).
  45. A. P. Bradley, The use of the area under the roc curve in the evaluation of machine learning algorithms, Pattern recognition 30 (1997) 1145–1159.
  46. The torgo database of acoustic and articulatory speech from speakers with dysarthria, Language Resources and Evaluation 46 (2012) 523–541.
  47. Dysarthric speech database for universal access research, in: Ninth Annual Conference of the International Speech Communication Association, 2008.
  48. Dysarthric speech database for development of qolt software technology., in: LREC, 2012, pp. 3378–3381.
  49. The nemours database of dysarthric speech, in: Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP’96, volume 3, IEEE, 1996, pp. 1962–1965.
  50. R. Kent, K. Hustad, Speech production: development (2009).
  51. On the selection of non-invasive methods based on speech analysis oriented to automatic alzheimer disease diagnosis, Sensors 13 (2013) 6730–6745.
  52. S. Luz, Longitudinal monitoring and detection of alzheimer’s type dementia from spontaneous speech data, in: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), IEEE, 2017, pp. 45–46.
  53. The influence of speaking rate on vowel space and speech intelligibility for individuals with amyotrophic lateral sclerosis, Journal of Speech, Language, and Hearing Research 38 (1995) 1001–1013.
  54. Acoustic and intelligibility characteristics of sentence production in neurogenic speech disorders, Folia Phoniatrica et Logopaedica 53 (2001) 1–18.
  55. A review: Devnagri speech to text for marathwada region, in: Proceedings of International Conference on Wireless Communication: ICWiCOM 2019, Springer, 2020, pp. 525–532.
  56. A. A. Joshy, R. Rajan, Automated dysarthria severity classification: A study on acoustic features and deep learning techniques, IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022) 1147–1157.
  57. Automatic severity classification of dysarthric speech by using self-supervised model with multi-task learning, in: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023, pp. 1–5.
  58. Speech intelligibility assessment of dysarthria using fisher vector encoding, Computer Speech & Language 77 (2023) 101411.
  59. Automatic severity classification of korean dysarthric speech using phoneme-level pronunciation features., in: Interspeech, 2021, pp. 4838–4842.
  60. Automated diagnosis and assessment of dysarthric speech using relevant prosodic features, in: Transactions on Engineering Technologies: Special Volume of the World Congress on Engineering 2013, Springer, 2014, pp. 529–542.
  61. B. A. Al-Qatab, M. B. Mustafa, Classification of dysarthric speech according to the severity of impairment: an analysis of acoustic features, IEEE Access 9 (2021) 18183–18194.
  62. Automatic assessment of dysarthria severity level using audio descriptors, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2017, pp. 5070–5074.
  63. Dysarthria detection and severity assessment using rhythm-based metrics., in: INTERSPEECH, 2020, pp. 2897–2901.
  64. Teager energy cepstral coefficients for classification of dysarthric speech severity-level, in: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2022, pp. 1462–1468.
  65. M. S. Paja, T. H. Falk, Automated dysarthria severity classification for improved objective intelligibility assessment of spastic dysarthric speech, in: Thirteenth Annual Conference of the International Speech Communication Association, 2012.
  66. Cross-lingual dysarthria severity classification for english, korean, and tamil, in: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2022, pp. 566–574.
  67. Discriminative prosodic features to assess the dysarthria severity levels, in: Proceedings of the World Congress on Engineering, volume 3, 2013.
  68. An automatic diagnosis and assessment of dysarthric speech using speech disorder specific prosodic features, in: 2016 39th International Conference on Telecommunications and Signal Processing (TSP), IEEE, 2016, pp. 515–518.
  69. Dysarthric speech recognition using dysarthria-severity-dependent and speaker-adaptive models., in: Interspeech, 2013, pp. 3622–3626.
  70. Weak speech supervision: A case study of dysarthria severity classification, in: 2020 28th European Signal Processing Conference (EUSIPCO), IEEE, 2021, pp. 101–105.
  71. Significance of energy features for severity classification of dysarthria, in: Speech and Computer: 24th International Conference, SPECOM 2022, Gurugram, India, November 14–16, 2022, Proceedings, Springer, 2022, pp. 325–337.
  72. Acoustic identification of sentence accent in speakers with dysarthria: cross-population validation and severity related patterns, Brain Sciences 11 (2021) 1344.
  73. K. Gurugubelli, A. K. Vuppala, Analytic phase features for dysarthric speech detection and intelligibility assessment, Speech Communication 121 (2020) 1–15.
  74. A. A. Joshy, R. Rajan, Automated dysarthria severity classification using deep learning frameworks, in: 2020 28th European Signal Processing Conference (EUSIPCO), IEEE, 2021, pp. 116–120.
  75. N. P. Narendra, P. Alku, Automatic intelligibility assessment of dysarthric speech using glottal parameters, Speech Communication 123 (2020) 1–9.
  76. Cross-database models for the classification of dysarthria presence., in: Interspeech, 2017, pp. 3127–3131.
  77. Fully automated speaker identification and intelligibility assessment in dysarthria disease using auditory knowledge, Biocybernetics and Biomedical Engineering 36 (2016) 233–247.
  78. On the relevance of using rhythmic metrics and svm to assess dysarthric severity, International Journal of Biometrics 6 (2014) 248–271.
  79. Statistical analysis of speech disorder specific features to characterise dysarthria severity level, in: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023, pp. 1–5.
  80. Wav2vec-based detection and severity level classification of dysarthria from speech, in: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023, pp. 1–5.
  81. Assessing dysarthria severity using global statistics and boosting, in: 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), IEEE, 2011, pp. 1103–1106.
  82. Continuous wavelet transform for severity-level classification of dysarthria, in: Speech and Computer: 24th International Conference, SPECOM 2022, Gurugram, India, November 14–16, 2022, Proceedings, Springer, 2022, pp. 312–324.
  83. Towards improving the performance of dysarthric speech severity assessment system, in: 2022 International Conference on Computer Communication and Informatics (ICCCI), IEEE, 2022, pp. 1–6.
  84. Assessment of dysarthria using one-word speech recognition with hidden markov models, Journal of Korean medical science 34 (2019).
  85. A. A. Joshy, R. Rajan, Dysarthria severity assessment using squeeze-and-excitation networks, Biomedical Signal Processing and Control 82 (2023) 104606.
  86. Investigation of different time-frequency representations for intelligibility assessment of dysarthric speech, Ieee transactions on neural systems and rehabilitation engineering 28 (2020) 2880–2889.
  87. Speech task based automatic classification of als and parkinson’s disease and their severity using log mel spectrograms, in: 2020 international conference on signal processing and communications (SPCOM), IEEE, 2020, pp. 1–5.
  88. M. Fernández-Díaz, A. Gallardo-Antolín, An attention long short-term memory based system for automatic classification of speech intelligibility, Engineering Applications of Artificial Intelligence 96 (2020) 103976.
  89. F. J. Montalbo, Dysarnet: A densely squeezed-and-excited attention-gated residual deep learning model for dysarthric speech recognition and severity estimation, Available at SSRN 4442941 (January 2023).
  90. Residual neural network precisely quantifies dysarthria severity-level based on short-duration speech segments, Neural Networks 139 (2021) 105–117.
  91. Spectro-temporal representation of speech for intelligibility assessment of dysarthria, IEEE Journal of Selected Topics in Signal Processing 14 (2019) 390–399.
  92. S. Akkoç, An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (anfis) model for credit scoring analysis: The case of turkish credit card data, European Journal of Operational Research 222 (2012) 168–178.
  93. A. Subasi, Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction, Computers in biology and medicine 37 (2007) 227–244.
  94. O. Geman, Data processing for parkinson’s disease: Tremor, speech and gait signal analysis, in: 2011 E-Health and Bioengineering Conference (EHB), IEEE, 2011, pp. 1–4.
  95. A. P. Accardo, E. Mumolo, An algorithm for the automatic differentiation between the speech of normals and patients with friedreich’s ataxia based on the short-time fractal dimension, Computers in biology and medicine 28 (1998) 75–89.
  96. F. Rudzicz, Towards a noisy-channel model of dysarthria in speech recognition, in: Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies, 2010, pp. 80–88.
  97. Recurrence quantification analysis of sentence-level speech kinematics, Journal of Speech, Language, and Hearing Research 59 (2016) 1315–1326.
  98. Acoustic change over time in spastic and/or flaccid dysarthria in motor neuron diseases, Journal of Speech, Language, and Hearing Research 65 (2022) 1767–1783.
  99. Raw source and filter modelling for dysarthric speech recognition, in: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2022, pp. 7377–7381.
  100. M. Vainio, T. Altosaar, Modeling the microprosody of pitch and loudness for speech synthesis with neural networks., in: ICSLP, 1998.
  101. Articulatory kinematic characteristics across the dysarthria severity spectrum in individuals with amyotrophic lateral sclerosis, American Journal of Speech-Language Pathology 27 (2018) 258–269.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now