MEDICAL TRANSCRIPTION USING SPEECH RECOGNIZER

Authors

  • Mahidhar B V S

DOI:

https://doi.org/10.20894/IJMSR.117.009.001.032

Keywords:

Speaker Recognition; Sub region Model; Model Synthesis.

Abstract

Speech recognition is a vital part in medical transcriptions. The existing speech recognition systems that run as standalone desktop applications fall short in many cases due to low accuracy rates and high processing time. This paper proposes a novel collaborative approach for the automation of speech to text conversion. The model proposed leverages the power of cloud computing and the unique nature of computing. This framework has many real time applications such as in Medical Transcription systems, IVR systems etc., The generic framework here is advantageous, because the speech models in Automatic Speech Recognizer (ASR) could be trained according to specific domain required, allowing wide usability. The proposed speech framework is used for medical transcription process which is automated by using the proposed speech framework. With this system, an android application was developed which acts as a medium between doctors and their patients. The application helps diagnose the patients using their symptoms. The doctors prescribe medicines through this
application to patients for various kinds of illness. It is user friendly and secure as it can be used by anyone round the clock. The entire model is developed for a mobile cloud environment considering the characteristics of cloud delivery models.

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Author Biography

Mahidhar B V S

Department of Computer Science Engineering, SRM University, Ramapuram, Chennai – 89

References

[1] A. Kanagasundaram, R. Vogt, D. B. Dean, S. Sridharan, and M. W. Mason, “i-vector based speaker recognition on short utterances,” in Proceedings of the 12th Annual Conference of the International Speech Communication Association. International SpeechCommunication Association (ISCA), 2011, pp. 2341–2344.

[2] J.-Y. Zhang, T. F. Zheng, J. Li, C.-H. Luo, and G.-L. Zhang, “Improved contextdependent acoustic modeling for continuous chinese speech recognition.” in Proc.
INTERSPEECH’01, 2001, pp. 1617–1620.

[3] I. P. Association, Handbook of the International Phonetic Association: A guide to the use of the International Phonetic Alphabet. Cambridge University, Press, 1999.

[4] T. Schultz and A. Waibel, “Language-independent and language adaptive acoustic modeling for speech recognition,” Speech Communication, vol. 35, no. 1, pp. 31–s51, 2001.

[5] `A. Colom´e, “Lexical activation in bilinguals’ speech production: Language-specific or language-independent?” Journal of memory and language, vol. 45, no. 4, pp. 721–736, 2001.

[6] N. Fatima, X.-J. Wu, T. F. Zheng, C.-H. Zhang, and G. Wang, “A universal phoneme-set based language independent short utterance speaker recognition,” in 11th National Conference on Man-Machine Speech Communication (NCMMSC’11), Xi’an, China, 2011, pp. 16– 18.

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Published

2017-03-27

Issue

Vol. 9 No. 1 (2017)

Section

Articles

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