Adaptive Machine Learning Models for Voice-Activated Search Interfaces

  • Authors

    • Dr. Venkatesh Iyer Professor, SRM Institute of Science and Technology, India. Author
    • Dr. Nandhini Ravi Assistant Professor, VIT University, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2018PI1L8M

    Published 01-03-2018

  • Adaptive Machine Learning, Voice-Activated Search Interfaces, Personalized Search, Reinforcement Learning, Online Learning, Transfer Learning, User Interaction Analysis, Speech Pattern Recognition, Data Privacy, Computational Resources

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

    How to Cite

    [1]
    V. Iyer and N. Ravi, “Adaptive Machine Learning Models for Voice-Activated Search Interfaces”, IJMLPA, vol. 1, no. 1, pp. 01–07, Jan. 2018, doi: 10.67228/3142788X/IJMLPA-2018PI1L8M.

  • Abstract

    Voice-activated search interfaces have become integral components of modern human-computer interactions, offering users hands-free and efficient methods to access information. This paper explores the development and implementation of adaptive machine learning models aimed at enhancing the responsiveness and accuracy of these interfaces. By analyzing user interactions and learning from individual speech patterns, these models aim to provide personalized and contextually relevant search results. The study delves into various machine learning techniques, including reinforcement learning, online learning, and transfer learning, assessing their effectiveness in adapting to user preferences and improving search accuracy. Challenges such as data privacy concerns, computational resource requirements, and user variability are also discussed, alongside potential solutions and future research directions.​

  • References

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