Voice Search Optimization using Deep Reinforcement Learning

  • Authors

    • Ritu Agarwal Finance Manager, Capgemini, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PI7R2M

    Published 05-03-2022

  • Voice Search Optimization, Deep Reinforcement Learning, User Interaction, Dynamic Adaptation, Speech Recognition, Natural Language Processing, Machine Learning, Personalized Search Systems

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    R. Agarwal, “Voice Search Optimization using Deep Reinforcement Learning”, IJMLPA, vol. 5, no. 1, pp. 01–07, May 2022, doi: 10.67228/3142788X/IJMLPA-2022PI7R2M.

  • Abstract

    Voice search has become an integral part of modern human-computer interaction, necessitating systems that can accurately and efficiently interpret spoken queries. Traditional voice search models often rely on static algorithms that may not adapt well to diverse user behaviors and evolving language patterns. This paper explores the application of Deep Reinforcement Learning (DRL) to optimize voice search systems. By leveraging DRL, voice search agents can learn from user interactions, dynamically adjusting to preferences and improving performance over time. We present a framework that integrates DRL into voice search optimization, discuss the challenges and considerations in implementing such systems, and highlight future research directions.​

  • References

    [1] Lin, B. (2022). Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook. arXiv preprint arXiv:2210.13623.

    [2] Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., & Jurafsky, D. (2016). Deep Reinforcement Learning for Dialogue Generation. arXiv preprint arXiv:1606.01541.

    [3] Uc-Cetina, V., Navarro-Guerrero, N., Martin-Gonzalez, A., Weber, C., & Wermter, S. (2021). Survey on reinforcement learning for language processing. arXiv preprint arXiv:2104.05565.

    [4] Haj-Ali, A., Ahmed, N. K., Willke, T., Gonzalez, J., Asanovic, K., & Stoica, I. (2019). A View on Deep Reinforcement Learning in System Optimization. arXiv preprint arXiv:1908.01275.

    [5] Baihan Lin. (2022). Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook. arXiv preprint arXiv:2210.13623.

    [6] Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., & Jurafsky, D. (2016). Deep Reinforcement Learning for Dialogue Generation. arXiv preprint arXiv:1606.01541.

    [7] Uc-Cetina, V., Navarro-Guerrero, N., Martin-Gonzalez, A., Weber, C., & Wermter, S. (2021). Survey on reinforcement learning for language processing. arXiv preprint arXiv:2104.05565.

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