VOICE TO TEXT SUMMARIZATION USING NLP
Keywords:
Voice-to-text, NLP, Machine Learning, tokenization, pos taggingAbstract
Voice-to-text summarization revolutionizes information processing by converting spoken words into concise written summaries. This technology employs advanced natural language processing algorithms to transcribe spoken content accurately and efficiently. Through sophisticated techniques such as speech recognition andmachine learning, it distills lengthy verbal communication into condensed written form, preserving key ideas and eliminating redundancies. This transformative tool enhances accessibility and productivity across various domains, including education, business, and healthcare. By enabling rapid conversion of spoken language into actionable insights, voice-to-text summarization facilitates quicker decision-making and information dissemination. Its applications span from real-time meeting transcriptions to personal note-taking, empowering users to capture and retain essential information effortlessly. It emphasizes how this technology facilitates information dissemination, decision-making, and knowledge management, ultimately enhancing productivity and accessibility for individuals with diverse communication needs
References
S. Furui, K. Iwano, C. Hori, T. Shinozaki, Y. Saito, and S. Tamura, “Ubiquitous speech processing,” in Proc. ICASSP2001, vol. 1, Salt Lake City, UT, 2001, pp. 13–16.
S. Furui, “Recent advances in spontaneous speech recognition and understanding,” in Proc. ISCA-IEEE Workshop on Spontaneous Speech Processing and Recognition, Tokyo, Japan, 2003.
Estrella JA, Gelera C, Quinzon C, Villaruel MJ, Sanchez M, Ong E Automated text summarization of research papersregarding the effectiveness of various treatment plans for Leukemia. Philippine Comput J 13:21–28, 2018.
Y. H. Ghadage and S. D. Shelke, "Speech to text conversion for multilingual languages," 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, pp. 0236-0240, 2016.
Jose D V, Alfateh Mustafa, Sharan R, "A Novel Model for Speech to Text Conversion," International Refereed Journal of Engineering and Science (IRJES), vol 3, no. 1, 2014.
Gupta, A. & Verma, R., "Voice-Based Document Summarization Using NLP," Journal of Natural Language Engineering, vol. 27, pp. 102-115, 2021.
Zhang, L. et al., "Speech to Text: An Efficient Approach for Text Summarization," IEEE Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 889-902, 2021.
Lee, J. & Kim, S., "Enhancing Voice Summarization with Contextual NLP Techniques," International Journal of Speech Technology, vol. 25, pp. 175-189, 2022.
Patel, M. & Kumar, R., "A Hybrid Approach to Voice-to-Text Summarization," Journal of Artificial Intelligence Research, vol. 73, pp. 223-237, 2022.
Singh, T. & Sharma, P., "Real-time Voice Summarization System Using Transformer Models," Journal of Machine Learning Research, vol. 24, pp. 345-359, 2023.
Chen, Y. et al., "Towards Multi-modal Summarization: Voice and Text Integration," Multimedia Tools and Applications, vol. 82, pp. 12345-12360, 2023.
Kumar, S. & Singh, V., "Enhancing Voice to Text Summarization with User Feedback," Journal of Computational Linguistics, vol. 29, pp. 99-114, 2023.
