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Effective speech recognition for healthcare industry using phonetic system

Gulbakshee Dharmale, Dipti D. Patil, Tanaya Ganguly, Nitin Shekapure

Abstract


The automatic speech recognition helps to achieve today’s demands such as flexibility in patient care, efficiency, medical records. ASR allows more effective use and combination of process management devices and systems. Because speech interaction is contactless, they can be seamlessly combined into a current hardware environment. This paper presents the phonetic system that implemented to improve the automatic speech recognition with higher accuracy for increasing performance. The system obtains input speech by a mic then works on the tried speech to recognize the spoken word. After that, it passes the ensuing text to the HMM classifier. The HMM classifier compares occurrence of the accredited word with probability map. The word with the highest probability of occurrence gets selected. It then substitutes accredited word with this utterance; this process is carried out for the entire accredited text. The phonetic system directly obtains and translates speech to text by providing 8% improvement in the accuracy of the system. Smart text independent multi-lingual SMS system is developed using phonetic system, which allows the user to convert their voice into text and send message. STIM SMS system can offer a very spirited substitute to traditional keyboard.


Keywords


healthcare industry; Fourth Industrial Revolution; machine learning; automatic speech recognition; Hidden Markov Model (HMM); mobile computing; neural network

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References


1. C Chatterjee I. Artificial Intelligence and Patentability: Review and Discussions. International Journal of Modern Research 2021; 1: 15-21.

2. Lee C. Speech Recognition and Production by Machines. International Encyclopedia of the Social & Behavioral Sciences (Second Edition). Elsevier, Oxford; 2015.pp. 695-702.

3. Singh PD, Kaur R, Dhiman G, et al. BOSS: A new QoS aware block chain assisted framework for secure and smart healthcare as a service. Expert Systems. 2021, 40(4). doi: 10.1111/exsy.12838

4. Lokhande MP, Patil DD, Patil LV, et al. Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery. Chakraborty C, ed. Security and Communication Networks. 2021, 2021: 1-16. doi: 10.1155/2021/5287514

5. Shekapure S, Shekapure N, Dharmale GJ, etal.Clinical Data Analysis of Patient and Recommendation. NeuroQuantology. 2022, 20(6): 6148-615. doi: 10.14704/nq.2022.20.6. NQ22619

6. Shinde AV, Patil DD. A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning. Healthcare Analytics. 2023, 4: 100211. doi: 10.1016/j.health.2023.100211

7. Bobde SP, Mantri ST, Patil DD, et al. Cognitive Depression Detection Methodology Using EEG Signal Analysis. Advances in Intelligent Systems and Computing. Published online 2018: 557-566. doi: 10.1007/978-981-10-7245-1_55

8. Singh P, Kaur R. An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19. Global Transitions. 2020, 2: 283-292. doi: 10.1016/j.glt.2020.11.002

9. Reddy BR, Mahender E. Speech to text conversion using android platform. International Journal of Engineering Research and Applications (IJERA) 2013; 3(1): 253-258.

10. Zhu M, Liao J, Liu J, et al. FedOSS: Federated Open Set Recognition via Inter-client Discrepancy and Collaboration. IEEE Transactions on Medical Imaging. Published online 2023: 1-1. doi: 10.1109/tmi.2023.3294014

11. Chen Z, Yang C, Zhu M, et al. Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data. IEEE Transactions on Medical Imaging. 2022, 41(12): 3663-3674. doi: 10.1109/tmi.2022.3192483

12. Zhu M, Chen Z, Yuan Y. FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting. IEEE Transactions on Medical Imaging. 2023, 42(6): 1632-1643. doi: 10.1109/tmi.2023.3235757

13. Dharmale G, Thakare V, Patil D D. Intelligent hands free speech based SMS system on Android. 2016 International Conference on Advances in Human Machine Interaction (HMI). Published online March 2016. doi: 10.1109/hmi.2016.7449177

14. Jeeva Priya K, Sree SS, Navya TVS, et al. Implementation of Phonetic Level Speech Recognition in Kannada Using HTK. 2018 International Conference on Communication and Signal Processing (ICCSP). Published online April 2018. doi: 10.1109/iccsp.2018.8524192

15. Bolla DR, Shivashankar, Pavan TS, et al. Voice enabled gadget assistance system for physically challenged and old age people. 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). Published online May 2017. doi: 10.1109/rteict.2017.8256966

16. Karpagavalli S, Chandra E. Phoneme and word based model for tamil speech recognition using GMM-HMM. 2015 International Conference on Advanced Computing and Communication Systems. Published online January 2015. doi: 10.1109/icaccs.2015.7324119

17. Dharmale G, Patil DD, Vilas M. Thakare Implementation of Efficient Speech Recognition System on Mobile Device for Hindi and English Language. International Journal of Advanced Computer Science and Applications 2019; 10(2): 83-87.doi: 10.14569/ijacsa.2019.0100212

18. Patil DD, Wadhai V M. Real-Time Meta Learning Approach for Mobile Healthcare. Advances in Intelligent Systems and Computing. Published online November 20, 2018: 11-23. doi: 10.1007/978-981-13-2414-7_2

19. Dharmale G, Shirsath P, Shinde A, et al. REMICARE—Medicine Intake Tracker and Healthcare Assistant. Proceedings of 3rd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Published online 2023: 273-283. doi: 10.1007/978-981-19-6088-8_25

20. Thalengala A, Shama K. Study of sub-word acoustical models for Kannada isolated word recognition system. International Journal of Speech Technology. 2016, 19(4): 817-826. doi: 10.1007/s10772-016-9374-0

21. Patil UG, Shirbahadurkar SD, Paithane AN. Automatic Speech Recognition of isolated words in Hindi language using MFCC. 2016 International Conference on Computing, Analytics and Security Trends (CAST). Published online December 2016. doi: 10.1109/cast.2016.7915008

22. Aggarwal RK, Dave M. Integration of multiple acoustic and language models for improved Hindi speech recognition system. International Journal of Speech Technology. 2012, 15(2): 165-180. doi: 10.1007/s10772-012-9131-y

23. Supriya S, Handore SM. Speech recognition using HTK toolkit for Marathi language. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). Published online September 2017. doi: 10.1109/icpcsi.2017.8391979

24. Narkhede A, Nemade MU. Efficient Method for Isolated Marathi Digits Recognition using DWT and Soft Computing Techniques. 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU). Published online February 2018. doi: 10.1109/iot-siu.2018.8519893

25. Malewadi D, Ghule G. Development of Speech recognition technique for Marathi numerals using MFCC & LFZI algorithm. 2016 International Conference on Computing Communication Control and automation (ICCUBEA). Published online August 2016. doi: 10.1109/iccubea.2016.7860099

26. Kalamani M, Krishnamoorthi M, Valarmathi RS. Continuous Tamil Speech Recognition technique under non stationary noisy environments. International Journal of Speech Technology. 2018, 22(1): 47-58. doi: 10.1007/s10772-018-09580-8

27. Mannepalli K, Sastry PN, Suman M. MFCC-GMM based accent recognition system for Telugu speech signals. International Journal of Speech Technology. 2015, 19(1): 87-93. doi: 10.1007/s10772-015-9328-y

28. Bhowmik T, Mandal SKD. Manner of articulation based Bengali phoneme classification. International Journal of Speech Technology. 2018, 21(2): 233-250. doi: 10.1007/s10772-018-9498-5

29. Tailor JH, Shah DB. Speech Recognition System Architecture for Gujarati Language. International Journal Computer Applications. 2016, 138(12): 28-31. doi: 10.5120/ijca2016909049

30. Londhe ND, Kshirsagar GB. Chhattisgarhi speech corpus for research and development in automatic speech recognition. International Journal of Speech Technology. 2018, 21(2): 193-210. doi: 10.1007/s10772-018-9496-7

31. Koolagudi SG, Bharadwaj A, Srinivasa Murthy YV, et al. Dravidian language classification from speech signal using spectral and prosodic features. International Journal of Speech Technology. 2017, 20(4): 1005-1016. doi: 10.1007/s10772-017-9466-5

32. Bharali SS, Kalita SK. Speech recognition with reference to Assamese language using novel fusion technique. International Journal of Speech Technology. 2018, 21(2): 251-263. doi: 10.1007/s10772-018-9501-1

33. Zia T, Zahid U. Long short-term memory recurrent neural network architectures for Urdu acoustic modeling. International Journal of Speech Technology. 2018, 22(1): 21-30. doi: 10.1007/s10772-018-09573-7

34. Guglani J, Mishra AN. Continuous Punjabi speech recognition model based on Kaldi ASR toolkit. International Journal of Speech Technology. 2018, 21(2): 211-216. doi: 10.1007/s10772-018-9497-6

35. Mittal P, Singh N. Development and analysis of Punjabi ASR system for mobile phones under different acoustic models. International Journal of Speech Technology. 2019, 22(1): 219-230. doi: 10.1007/s10772-019-09593-x

36. Kadyan V, Mantri A, Aggarwal RK, et al. A comparative study of deep neural network based Punjabi-ASR system. International Journal of Speech Technology. 2018, 22(1): 111-119. doi: 10.1007/s10772-018-09577-3

37. Zhu M, Liao J, Liu J, et al. FedOSS: Federated Open Set Recognition via Inter-client Discrepancy and Collaboration. IEEE Transactions on Medical Imaging. Published online 2023: 1-1. doi: 10.1109/tmi.2023.3294014

38. Center For Indian Languages Technology, IIT Bombay. Available online: www.cfilt.iitb.ac.in>hindi_version (accessed on 22 August 2023)

39. Dharmale G, Patil DD, Thakare VM, et.al., Performance evaluation of different ASR Classifiers on Mobile Device. International Journal of Next-Generation Computing-Special Issue, 2021; 12(2).

40. Dharmale G J, Patil DD. Evaluation of Phonetic System for Speech Recognition on Smartphone. International Journal of Innovative Technology and Exploring Engineering. 2019, 8(10): 3354-3359. doi: 10.35940/ijitee.j1215.0881019

41. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006, 27(8): 861-874. doi: 10.1016/j.patrec.2005.10.010




DOI: https://doi.org/10.32629/jai.v7i5.1019

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Copyright (c) 2024 Gulbakshee Dharmale, Dipti D. Patil, Tanaya Ganguly, Nitin Shekapure

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