Survey on Virtual Assistant Using Python
Keywords:
Python, Speech Recognition, API Integration, NLPAbstract
Advancement of technology in this digital world, daily life has become smarter and easier. This also includes virtual assistants who can help with many simple tasks by teaching them to do it. Here the command is given as input, The output is the result of the work in the form of speech or displayed on the screen and sometimes on the screen. Input is provided by the microphone (Bluetooth cable already installed). There are many famous virtual assistants such as Amazon - Alexa, Apple – Siri, Microsoft - Cortana, Samsung - Bixby. Used by humans and some. Although it supports many languages. A virtual assistant is a combination of speech recognition. (Convert text to speech), command and various functions time, extra time, alarm clock, etc. The virtual assistant can chat with people (chatbot), daily schedule, reminders, notes, calculator, jokes, web content, alarm clock, open and close applications and documents, etc. It also connects to the Internet to provide results for user queries.
Downloads
References
[1]M. Bapat H. Gune, and P. Bhattacharyya, “A Paradigm-Based Finite State Morphological Analyzer For Marathi,” in Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing (WSSANLP), pp. 26–34, 2010.
[2] B. S. Atal and L. R. Rabiner, “A Pattern Recognition Approach to Voiced Unvoiced- Silence Classification with Applications to Speech Recognition,” Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 24, no. 3, pp. 201–212, 1976.
[3] V. Radha and C. Vimala, “A Review on Speech Recognition Challenges and Approaches,” doaj. Org, vol. 2, no. 1, pp. 1–7, 2012.
[4] T. Schultz and A. Waibel, “Language- Independent and Language Adaptive Acoustic Modeling for Speech Recognition”, Speech Communication, vol. 35, no. 1, pp. 31–51,
2001.
[5] J. B. Allen, “From Lord Rayleigh to Shannon: How Do Humans Decode Speech,” in International Conference on Acoustics, Speech and Signal Processing, 2002.
[6] M. Bapat, H. Gune, and P. Bhattacharyya, “A Paradigm-Based Finite State Morphological Analyzer For Marathi,” in Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing (WSSANLP), pp. 26–34, 2010.
[7] G. Muhammad, Y. Alotaibi, M. N. Huda, et al., pronunciation variation for asr: A survey of the “Automatic Speech Recognition for Bangla Digits, Literature” Speech Communication, vol. 29, no. in Computers and Information Technology, 2009.2, pp. 225– 246, 1999.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal for Research Publication and Seminar

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.