Real-time Search Query Understanding in Voice Assistants via AI Models

  • Authors

    • Dr. Yuki Nakamura Associate Professor, Kyoto University, Japan. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2021PII3R7X

    Published 08-03-2021

  • Voice Assistants, Real-Time Query Understanding, AI Models, Natural Language Processing (NLP), Deep Learning, Speech Recognition, Transformer Models, Reinforcement Learning, Contextual Understanding, Query Processing

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    Y. Nakamura, “Real-time Search Query Understanding in Voice Assistants via AI Models”, IJMLPA, vol. 4, no. 2, pp. 01–10, Aug. 2021, doi: 10.67228/3142788X/IJMLPA-2021PII3R7X.

  • Abstract

    Voice assistants have become an integral part of modern life, offering users the ability to interact with technology via voice commands. A critical aspect of these systems is the ability to understand and process real-time search queries effectively. Real-time search query understanding requires fast, accurate interpretation of spoken language, often in noisy and dynamic environments. This paper explores the role of artificial intelligence (AI) models in enhancing the understanding of real-time voice queries. Specifically, it focuses on the application of advanced AI techniques, including deep learning, natural language processing, and reinforcement learning, to improve query comprehension and response generation in voice assistants. We discuss the challenges faced in real-time systems, such as handling contextual ambiguity and speech recognition errors, and review various AI models that have been proposed or implemented to address these challenges. Furthermore, we evaluate the performance of these models and their impact on the user experience in real-world applications.

  • References

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