A Review of Automatic Speech Recognition Technology and its Applications in the Medical Field
DOI:
https://doi.org/10.47611/jsrhs.v14i1.8753Keywords:
Automatic Speech Recognition, Neural Networks, Cochlear Implants, Neurodegenerative Disorders, Dyslexia TherapyAbstract
The last few years have brought significant advances in Automatic Speech Recognition (ASR) technology due to the rise of deep learning technology. The paper discusses various challenges relating to memory usage and computing power required for ASR are discussed as well as novel methods for combating them. It also highlights the implementation of ASR in embedded devices in the medical field. It discusses its diagnostics capabilities for various neurodegenerative disorders and its role in improving life for those afflicted with deafness or dyslexia through cochlear implants and reading therapy. Despite significant improvements in such medical technology, there are still challenges such as lack of availability of data or noisy environments with reverberant conditions.
Downloads
References or Bibliography
Georgescu, Alexandru-Lucian, Alessandro Pappalardo, Horia Cucu, and Michaela Blott. 2021. “Performance vs. Hardware Requirements in State-of-The-Art Automatic Speech Recognition.” EURASIP Journal on Audio, Speech, and Music Processing 2021. https://doi.org/10.1186/s13636-021-00217-4
Cong, Shuang, and Yang Zhou. 2022. “A Review of Convolutional Neural Network Architectures and Their Optimizations.” Artificial Intelligence Review, June. https://doi.org/10.1007/s10462-022-10213-5
Zhao, Qiuming, Guangzhi Sun, Chao Zhang, Mingxing Xu, and Thomas Fang Zheng. 2024. Review of Enhancing Quantised End-To-End ASR Models via Personalisation. IEEE. March 18, 2024. https://doi.org/10.1109/ICASSP48485.2024.10448012
Nguyen, Hieu Duy, Anastasios Alexandridis, and Thanasis Mouchtaris. 2020. “Quantization Aware Training with Absolute-Cosine Regularization for Automatic Speech Recognition.” Amazon Science. 2020. https://doi.org/10.21437/Interspeech.2020-1991
Hazrati, Oldooz, Shabnam Ghaffarzadegan, and John H.L. Hansen. 2015. Review of Leveraging Automatic Speech Recognition in Cochlear Implants for Improved Speech Intelligibility under Reverberation. IEEE. August 6, 2015. https://doi.org/10.1109/ICASSP.2015.7178941
Neuman, Arlene C., Marcin Wroblewski, Joshua Hajicek, and Adrienne Rubinstein. 2010. “Combined Effects of Noise and Reverberation on Speech Recognition Performance of Normal-Hearing Children and Adults.” Ear and Hearing. https://doi.org/10.1097/aud.0b013e3181d3d514
Huang, Juan, Benjamin Sheffield, Payton Lin, and Fan-Gang Zeng. 2017. “Electro-Tactile Stimulation Enhances Cochlear Implant Speech Recognition in Noise.” Scientific Reports 7. https://doi.org/10.1038/s41598-017-02429-1
Fletcher, Mark D., Haoheng Song, and Samuel W. Perry. 2020. “Electro-Haptic Stimulation Enhances Speech Recognition in Spatially Separated Noise for Cochlear Implant Users.” Scientific Reports 10. https://doi.org/10.1038/s41598-020-69697-2
National Institute on Deafness and other Communication Disorders. 2021. “Cochlear Implants.” NIDCD. U.S. Department of Health and Human Services. March 24, 2021.
Fletcher, Mark D., Robyn O. Cunningham, and Sean R. Mills. 2020. “Electro-Haptic Enhancement of Spatial Hearing in Cochlear Implant Users.” Scientific Reports. https://doi.org/10.1038/s41598-020-58503-8
Pedersen, Jakob Schou, Lars Bo Larsen, and Børge Lindberg. 2008. “Usability of ASR-Based Reading Training for Dyslexics.” Interspeech 2008, September. https://doi.org/10.21437/Interspeech.2008-474
Husni, H., and Z. Jamaludin. 2010. Review of Minimizing Word Error Rate in a Dyslexic Reading-Oriented ASR Engine Using Phoneme Refinement and Alternative Pronunciation. EDULEARN10 Proceedings. 2010.
Lamptey, Richard, Bivek Chaulagain, Riddhi Trivedi, Avinash Gothwal, Buddhadev Layek, and Jagdish Singh. 2022. “A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics.” ProQuest 23. https://doi.org/10.3390/ijms23031851
Koeppen, Arnulf H. 2011. “Friedreich’s Ataxia: Pathology, Pathogenesis, and Molecular Genetics.” Journal of the Neurological Sciences. https://doi.org/10.1016/j.jns.2011.01.010
Dobson, R., and G. Giovannoni. 2019. “Multiple Sclerosis - a Review.” European Journal of Neurology 26. https://doi.org/10.1111/ene.13819
Schultz, Benjamin G., Venkata S. Aditya Tarigoppula, Gustavo Noffs, Sandra Rojas, Anneke van der Walt, David B. Grayden, and Adam P. Vogel. 2021. Review of Automatic Speech Recognition in Neurodegenerative Disease. International Journal of Speech Technology. May 4, 2021. https://doi.org/10.1007/s10772-021-09836-w
Schultz, Benjamin G., Zaher Joukhadar, Usha Nattala, Maria del Mar Quiroga, Gustavo Noffs, and Sandra Rojas. 2023. “Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics”. IEEE. October 4, 2023. https://doi.org/10.1109/tnsre.2023.3321874
Pan, Y, B Mirheidari, M Reuber, A Venneri, D Blackburn, and H Christensen. 2020. “Improving Detection of Alzheimer’s Disease Using Automatic Speech Recognition to Identify High-Quality Segments for More Robust Feature Extraction - White Rose Research Online.” http://dx.doi.org/10.21437/Interspeech.2020-2698
Bertini, Flavio, Allevi Davide, Lutero Gianluca, Danilo Montesi, and Calzà Laura. 2021. “Automatic Speech Classifier for Mild Cognitive Impairment and Early Dementia.” ACM Transactions on Computing for Healthcare. https://doi.org/10.1145/3469089
Oxenham, Andrew J. 2018. “How We Hear: The Perception and Neural Coding of Sound.” Annual Review of Psychology 69. https://doi.org/10.1146/annurev-psych-122216-011635
Published
How to Cite
Issue
Section
Copyright (c) 2025 Abhinav Sood; Dr. Manisha Srivastava
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
