Context awareness, the system's ability to comprehend and adapt to the user's surroundings, is harnessed through
cutting-edge natural language processing, environmental sensing, and machine learning algorithms. To achieve context
awareness, the proposed system employs advanced natural language processing and machine learning algorithms.
The incorporation of context-aware features allows voice assistants to grasp the situational nuances of a conversation. This
involves considering the user's prior commands, inquiries, and the broader context of the dialogue. Such awareness enables the
voice assistant to provide more relevant and coherent responses, creating a seamless and natural conversation flow.
Personalization plays a crucial role in making voice assistants not only responsive but also adaptive to the unique needs and
preferences of each user. Through the analysis of user behavior, preferences, and historical interactions, voice assistants can
learn and evolve over time, delivering a more personalized and user-centric experience. This tailored approach not only
enhances user satisfaction but also fosters a sense of connection between the user and the voice assistant.
In conclusion, the convergence of context-aware features and personalized responses represents a paradigm shift in voice
assistant design. This approach holds the potential to elevate user satisfaction, foster more natural and intuitive conversations,
and redefine the future landscape of voice interaction technology.
[1] Rospocher, M., van Erp, M., Vossen, P., Fokkens, A., Aldabe,I., Rigau, G., Soroa, A., Ploeger, T., and Bogaard,
T.(2016). “Building event-centric knowledge graphs from the news. Web Semantics: Science, Services, and Agents
on the World Wide Web” In Press.
[2] Knight, K., & Langkilde, I. “ Preserving ambiguities in generation via automata intersection” In
AAAI/IAAI (pp. 697-702).
[3] Liddy, E. D. (2001). Natural language processing.
[4] Che-Wei Huang, Roland Maas, Sri Harish Mallidi, Björn Hoffmeister., “A Study for Improving Device-Directed
Speech Detection toward Frictionless Human-Machine Interaction”, 2019.
[5] Thomas M. Brill, Laura Munoz & Richard J. Miller Siri, Alexa, and other digital assistants: “A study of
customer satisfaction with artificial intelligence applications”, 2019.
[6] D. Reich, F. Putze, D. Heger, J. Ijsselmuiden, R. Stiefelhagen, and T. Schultz, (2011) “A real-time speech
command detector for a smart control room,” in Proceedings of Interspeech
[7] E. Shriberg, A. Stolcke, D. Hakkani-Tr, and L. Heck, “Learning when to listen: Detecting system-addressed speech in
human-human-computer dialog,” in Proceedings of Interspeech, 2012.
[8] T. Yamagata, T. Takiguchi, and Ariki, “System request detection in human conversation based on multi-resolution Gabor
wavelet features,” in Proceedings of Interspeech, 2009.
[9] H. Lee, A. Stolcke, and E. Shriberg,(2013) “Using out-of-domain data for lexical addressee detection in human human
computer dialog,” in Proceedings of HLT-NAACL,
[10] Wang, D. Hakkani-Tur, and G. Tur “Understanding computer directed utterances in multi-user dialog
systems,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2013.
