The Audio Interaction Technique and Its Applications In Small-Size Electrical Appliances

Yiwei Cao

doi:10.54097/bfp8cr17

Authors

Yiwei Cao

DOI:

https://doi.org/10.54097/bfp8cr17

Keywords:

Audio Interaction Technology, ASR, NLP, TTS, Smart Home Applications.

Abstract

Audio interaction technology, which facilitates command of digital products through natural speech, serves as a cornerstone of modern intelligent systems, significantly enhancing daily life efficiency and user satisfaction. This paper details the foundational principles underpinning this technology, outlining the integrated three-stage process: Automatic Speech Recognition (ASR) decodes audio input into text, Natural Language Processing (NLP) analyzes this text to discern user intent and generate commands, and Text-to-Speech (TTS) synthesizes audible, human-like responses. The document then explores its transformative application and influence within the smart home ecosystem, providing specific analysis of its implementation in small-size appliances such as digital watches and smart speakers. These case studies demonstrate tangible benefits, including unparalleled hands-free convenience, enhanced safety, and improved accessibility for users with visual or mobility impairments. Finally, the paper discusses prospective future development directions, forecasting the mainstream adoption of low-power-consuming products and the continued expansion into new application scenarios, further optimizing and personalizing the domestic living experience.

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References

[1] Davis K. H., Biddulph R., Balashek S., et al. Automatic Recognition of Spoken Digits. The Journal of the Acoustical Society of America, 1952, 24 (6): 637–642.

[2] IBM Archives. IBM Shoebox. IBM Corporate Archives, 2023.

[3] Juang B. H., Rabiner L. R. Automatic Speech Recognition–A Brief History of the Technology Development. Elsevier Encyclopedia of Language and Linguistics, 2005.

[4] Hoy M. B., Alexa, Siri, Cortana, et al. An Introduction to Voice Assistants. Medical Reference Services Quarterly, 2018, 37 (1): 81–88.

[5] Huang W. B., Zhang M. Z., Sun J. Y., et al. Exploration of Application Security for Voice Recognition and Interaction Technology in Smart Home Appliances. Electric Appliances, 2021

[6] Xu M., Qian M., Sun Q., et al. Towards Energy Efficient Speech Recognition on Smartphones. 2020 IEEE/ACM Symposium on Edge Computing (SEC), 2020.

[7] Hinton G., Deng L., Yu D., et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 2012, 29 (6): 82–97.

[8] Devlin J., Chang M. W., Lee K., et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv, 2018:1810: 04805,

[9] van den Oord A., Dieleman S., Zen H., et al. WaveNet: A Generative Model for Raw Audio. arXiv preprint arXiv, 2016, 1609: 03499,

[10] Pradhan A., Mehta K., Findlater L. "Accessibility Came by Accident": Use of Voice-Controlled Intelligent Personal Assistants by People with Disabilities. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018.