Voice Assistant Using Python
Abstract
Today, technological innovation is accelerating. Previously, we could only perform a few things on a computer system. However, machine learning, artificial intelligence, deep learning, and a few other technologies have advanced computer systems to the point where we can accomplish any work. If individuals are still trying to communicate utilising numerous input devices in this day and age, it's not worth it. As a result, we created a voice assistant in Python that allows the user to run any type of command in Linux without using the keyboard. The primary function of a voice assistant is to reduce the use of input devices such as a keyboard, mouse, and so on. It will also save hardware space and money.
References
1. Bapat M, Gune H, Bhattacharyya P. A paradigm-based finite state morphological analyzer for marathi,” in Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing (WSSANLP), 26–34.
2010.
2. Atal BS, Rabiner LR. A pattern recognition approach to voiced unvoiced-silence classification with applications
to speech recognition Acoustics, Speech and Signal Processing, IEEE Transactions on 1976; 24(3): 201–212.
3. Radha V, Vimala C. A review on speech recognition challenges and approaches,” doaj. 2012; 2(1): 1–7.
4. T. Schultz and A. Waibel, “Language- independent and language adaptive acoustic modeling for speech recognition”, Speech Communication, vol. 35, no. 1,
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, P. Bhattacharyya, “A paradigmbased
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.
8. S. R. Eddy, “Hidden Markov models,” Current opinion in structural biology, vol. 6, no. 3, pp. 361–365, 1996.
9. excellent style manual for science writers is “Speech recognition with flat direct models,” IEEE Journal of Selected Topics in Signal Processing, 2010.
10. Srivastava S., Prakash S. (2020) Security Enhancementof IoT Based Smart Home Using Hybrid Technique.
In: Bhattacharjee A., Borgohain S., Soni B., Verma G., Gao XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi. org/10.1007/978-981-15-6318-8_44
11. S. Srivastava and S. Prakash, “An Analysis of Various IoT Security Techniques: A Review,” 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020, pp. 355- 362, doi: 10.1109/ ICRITO48877.2020.9198027
12. Saijshree Srivastava, Surya Vikram Singh, Rudrendra Bahadur Singh, Himanshu Kumar Shukla,” Digital Transformation of Healthcare: A blockchain study” International Journal of Innovative Science, Engineering
& Technology, Vol. 8 Issue 5, May 2021
2010.
2. Atal BS, Rabiner LR. A pattern recognition approach to voiced unvoiced-silence classification with applications
to speech recognition Acoustics, Speech and Signal Processing, IEEE Transactions on 1976; 24(3): 201–212.
3. Radha V, Vimala C. A review on speech recognition challenges and approaches,” doaj. 2012; 2(1): 1–7.
4. T. Schultz and A. Waibel, “Language- independent and language adaptive acoustic modeling for speech recognition”, Speech Communication, vol. 35, no. 1,
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, P. Bhattacharyya, “A paradigmbased
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.
8. S. R. Eddy, “Hidden Markov models,” Current opinion in structural biology, vol. 6, no. 3, pp. 361–365, 1996.
9. excellent style manual for science writers is “Speech recognition with flat direct models,” IEEE Journal of Selected Topics in Signal Processing, 2010.
10. Srivastava S., Prakash S. (2020) Security Enhancementof IoT Based Smart Home Using Hybrid Technique.
In: Bhattacharjee A., Borgohain S., Soni B., Verma G., Gao XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi. org/10.1007/978-981-15-6318-8_44
11. S. Srivastava and S. Prakash, “An Analysis of Various IoT Security Techniques: A Review,” 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020, pp. 355- 362, doi: 10.1109/ ICRITO48877.2020.9198027
12. Saijshree Srivastava, Surya Vikram Singh, Rudrendra Bahadur Singh, Himanshu Kumar Shukla,” Digital Transformation of Healthcare: A blockchain study” International Journal of Innovative Science, Engineering
& Technology, Vol. 8 Issue 5, May 2021
Published
2023-08-05
How to Cite
SAXENA, Anuj.
Voice Assistant Using Python.
Journal of Advanced Research in Applied Artificial Intelligence and Neural Network, [S.l.], v. 7, n. 1, p. 27-31, aug. 2023.
Available at: <http://thejournalshouse.com/index.php/neural-network-intelligence-adr/article/view/769>. Date accessed: 22 dec. 2024.
Section
Review Article
