Comparative Study for Virtual Personal Assistants (VPA) and State-of-the-Art Speech Recognition Technology
DOI:
https://doi.org/10.22399/ijcesen.383Keywords:
NLP (Natural Language Processing), AI (Artificial Intelligence), VPA (Virtual Personal Assistant), and SR (Speech Recognition)Abstract
Numerous types of virtual assistants have emerged as a result of the widespread use of smartphones, the expansion of their services, the tremendous advancements in automatic speech recognition and AI, and the growing reliance on virtual personal assistants (VPAs) for basic daily tasks like playing music, sending texts, making restaurant reservations, and getting weather updates. The popularity of virtual personal assistants is largely attributable to their convenient blend of user-friendliness and natural language interaction. This study comprehensively examines various virtual personal assistants powered by AI. It briefly overviews each, such as Microsoft Cortana, Samsung Bixby, Apple SIRI, Google Assistant, and Amazon Alexa. This study also includes a comprehensive overview of the state-of-the-art speech recognition used in virtual personal assistants. The findings show that each Virtual Personal Assistant has advantages, and a user may select any of them depending on his preferences and needs.
References
AV. López, (2019). Alexa & Google Assistant: The domestication and privacy implications of smart speakers and virtual personal assistants within the household (Doctoral dissertation, Vrije Universiteit Brussel).
S. Lieberam-Schmidt, (2010). Analyzing and Influencing Search Engine Results: Business and Technology Impacts on Web Information Retrieval. Springer Science & Business Media. DOI: 10.1007/978-3-8349-8915-4.
V. Kepuska, G. Bohouta, (2018). Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home). In2018 IEEE 8th annual computing and communication workshop and conference CCWC pp. 99-103. IEEE. DOI: 10.1109/CCWC.2018.8301638
A. Purington, JG. Taft, S. Sannon, NN. Bazarova, SH. Taylor, (2017). Alexa is my new BFF. social roles, user satisfaction, and personification of the Amazon Echo. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems (2017) May 6 pp. 2853-2859. DOI: 10.1145/3027063.3053246
F. Xie, Y. Zhang, C. Yan, S. Li, L. Bu, K. Chen, Z. Huang, G. Bai, (2022). Scrutinizing privacy policy compliance of virtual personal assistant apps. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering pp. 1-13. DOI: 10.1145/3551349.3560416
A. Mittal, A. Agrawal, A. Chouksey, R. Shriwas, S. Agrawal, (2016). A comparative study of chatbots and humans. Situations 2-2. DOI: 10.17148/IJARCCE.2016.53253
AL. Nobles, EC. Leas, TL. Caputi, SH. Zhu, SA. Strathdee, JW, Ayers, (2020). Responses to addiction help-seeking from Alexa, Siri, Google Assistant, Cortana, and Bixby intelligent virtual assistants. NPJ digital medicine 29;3-1:11. DOI: 10.1038/s41746-019-0215-9
SK. Gaikwad, BW. Gawali, P. Yannawar, (2010). A review on speech recognition technique. International Journal of Computer Applications 10-3;16-24. DOI:10.5120/1462-1976.
A. Ravi, K. Subramanian, M. Srivastava, (2017). Private communication.
R. Knote, A. Janson, M. Söllner, JM. Leimeister (2019). Classifying smart personal assistants: An empirical cluster analysis. DOI: 10.17170/kobra-202010302037.
AL. Nobles, EC. Leas, TL. Caputi, SH. Zhu, SA. Strathdee, JW. Ayers (2020). Responses to addiction help-seeking from Alexa, Siri,
Google Assistant, Cortana, and Bixby intelligent virtual assistants. NPJ digital medicine 29;3-1:11. DOI: 10.1038/s41746-019-0215-9.
O. Bahceci, (2016). Analysis and Comparison of Intelligent Personal Assistants. Manuscrito no publicado. http://kth. instructure. com/files/92243/download.
A. Reis, D. Paulino, H. Paredes, I. Barroso, MJ. Monteiro, V. Rodrigues, J. Barroso, (2018). Using intelligent personal assistants to assist the elderlies An evaluation of Amazon Alexa, Google Assistant, Microsoft Cortana, and Apple Siri. In2018 2nd International Conference on Technology and Innovation in Sports, Health and Wellbeing TISHW pp. 1-5. IEEE. DOI: 10.1109/TISHW.2018.8559503.
AS. Tulshan, SN. Dhage, (2018). Survey on virtual assistant: Google assistant, siri, cortana, alexa, In Advances in Signal Processing and Intelligent Recognition Systems: 4th International Symposium SIRS 2018, Bangalore, India, Revised Selected Papers 4, pp. 190-201. Springer Singapore, 2019. DOI: 10.1109/msp.2016.2617341.
AS. Goh, LL. Wong, KY. Yap, (2021). Evaluation of COVID-19 information provided by digital voice assistants, International Journal of Digital Health 1, no. 1. DOI: 10.29337/ijdh.25.
H. Jo, Hi Bixby: (2023). Determinants of goal-congruent usage and goal-congruent outcome in the artificial intelligence personal assistant context. Journal of Information Science 30;01655515231161554. DOI: 10.1177/0165551523116155.
CM. Seródio Figueiredo, T. de Melo, R. Goes, (2022). Evaluating voice assistants' responses to COVID-19 vaccination in portuguese: quality assessment. JMIR Human Factors (2022) Mar 21;9(1):e34674. DOI: 10.2196/34674.
U. da Silva Fernandes, GA. Barbosa, B. Azevedo, GD. Chagas, SD. Barbosa, RO. Prates, (2022). Lessons Learned from Modeling the Interaction with Conversational Agents DOI: 10.3389/fcomp.2022.744574.
AQ. Ahmad, MA. Jawad, KH. M. Jaber, (2022). E-learning issues and solutions for students with disabilities during COVID-19 pandemic: Al-Zaytoonah University of Jordan case study. International Journal of Evaluation and Research in Education 11-4;2087-94. DOI: 10.11591/ijere.v11i4.22842.
Khulood Abu Maria, Khalid Mohammad Jaber, and Mossab N. Ibrahim, (2018). A New Model for Arabic Multi-Document Text Summarization, International Journal of Innovative Computing, Information and Control (IJICIC), 14(4);1443–1452.
Tarek Kanan & Edward A. Fox, (2016). Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy, Journal of the Association for Information Science & Technology, Association for Information Science & Technology, 67(11);2667-2683.
M. N. Ibrahim, K. A. Maria and K. M. Jaber, (2017). A comparative study for Arabic Multi-Document Summarization Systems (AMD-SS). 8th International Conference on Information Technology (ICIT), Amman, Jordan, pp. 1013-1022, doi: 10.1109/ICITECH.2017.8079984.
Abdallah, M., Hammad, A., & AlZyadat, W. (2022). Towards a Data Collection Quality Model for Big Data Applications. Lecture Notes in Business Information Processing, 103–108. https://doi.org/10.1007/978-3-031-04216-4_11
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