Enhancing Robustness to Errors in AI-based Speech Recognition Kiosks 


Vol. 15,  No. 2, pp. 95-101, Feb.  2026
https://doi.org/10.3745/TKIPS.2026.15.2.95


PDF
  Abstract

This study proposes an integrated approach to enhance robustness to errors in AI-based speech recognition kiosks. While the adoption of voice-enabled kiosks has been rapidly increasing in unmanned service environments, recognition errors caused by background noise, diverse intonations, and pronunciation variations remain a critical barrier to reliable performance. In this research, the baseline speech recognition accuracy, which stagnated at approximately 60% when using only basic Natural Language Processing (NLP) techniques, was improved to over 94% by incorporating data augmentation, utterance filtering, context-based intent analysis, and error correction algorithms. The system preprocesses speech input and applies correction strategies to classify and mitigate frequent recognition errors. Experimental results demonstrate that the proposed method consistently improves recognition accuracy across diverse user environments, achieving a level of robustness suitable for real-world kiosk deployment. This study offers a practical solution that advances the quality of AI-driven speech interfaces and contributes to enhancing the accessibility and user experience of voice-based unmanned services.

  Statistics
  Cite this article

[IEEE Style]

J. S. Ryu and S. K. Jung, "Enhancing Robustness to Errors in AI-based Speech Recognition Kiosks," The Transactions of the Korea Information Processing Society, vol. 15, no. 2, pp. 95-101, 2026. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.95.

[ACM Style]

Ji Soo Ryu and Soon Ki Jung. 2026. Enhancing Robustness to Errors in AI-based Speech Recognition Kiosks. The Transactions of the Korea Information Processing Society, 15, 2, (2026), 95-101. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.95.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals