Voice Interaction Data for Training Large-scale Search Engine Models

  • Authors

    • Kenji Sato Engineering Director Sony Corporation, Japan. Author
    • Aiko Yamamoto Product Manager Panasonic, Japan. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJIARE-2022PI3P7C

    Published 04-02-2022

  • Voice Interaction Data, Search Engine Models, Speech Recognition, Natural Language Processing (NLP), Machine Learning, Multimodal Search, Voice Search, Conversational AI, Data Privacy And Ethics, Information Retrieval

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    K. Sato and A. Yamamoto, “Voice Interaction Data for Training Large-scale Search Engine Models”, IJMLPA, vol. 5, no. 1, pp. 01–07, Apr. 2022, doi: 10.67228/3142788X/IJIARE-2022PI3P7C.

  • Abstract

    With the growing popularity of voice-activated search and digital assistants, integrating voice interaction data into large-scale search engine models has become a vital area of research. This paper explores the potential of voice data for improving search engine performance, specifically in the context of large-scale models. Voice interaction data provides unique challenges, including variations in speech patterns, accents, background noise, and conversational context. However, when effectively harnessed, voice data can significantly enhance search accuracy, relevance, and user engagement. This paper discusses current methods of collecting, processing, and integrating voice data into search engine models, along with machine learning techniques like speech-to-text and intent recognition. We also highlight the ethical considerations and challenges associated with voice data collection. The future of voice-driven search is promising, with advancements in multi-modal search, personalized experiences, and more efficient processing capabilities.

  • References

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