skip to main content
10.1145/3472306.3478347acmconferencesArticle/Chapter ViewAbstractPublication PagesivaConference Proceedingsconference-collections
research-article

Designing Personality Shifting Agent for Speech Recognition Failure

Published:14 September 2021Publication History

ABSTRACT

This paper proposes a method to shift an agent's personality during speech interaction to reduce users' negative impressions of speech recognition systems when speech recognition fails. Speech recognition failure makes users uncomfortable, and the cognitive strain in rephrasing commands is high. The proposed method aims to eliminate users' negative impression of agents by allowing an agent to have multiple personalities and accept responsibility for the failure, with the personality responsible for failure being removed from the task. System hardware remains the same, and users can continue to interact with another personality of the agent. Shifting the agent's personality is represented by a change in voice tone and LED color. Experimental results suggested that the proposed method reduces users' negative impressions by improving communication between users and the agent.

References

  1. Murtaza Bulut, Shrikanth S Narayanan, and Ann K Syrdal. 2002. Expressive speech synthesis using a concatenative synthesizer. In Seventh International Conference on Spoken Language Processing.Google ScholarGoogle Scholar
  2. B Çürüklü, G Dodig-Crnkovic, and B Akan. 2010. Towards industrial robots with human-like moral responsibilities. In 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Institute of Electrical and Electronics Engineers, 85--86. https://doi.org/10.1109/HRI.2010.5453259 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Susan T Fiske. 1980. Attention and weight in person perception: The impact of negative and extreme behavior. Journal of personality and Social Psychology 38, 6 (1980), 889.Google ScholarGoogle ScholarCross RefCross Ref
  4. Stella George. 2019. From Sex and Therapy Bots to Virtual Assistants and Tutors: How Emotional Should Artificially Intelligent Agents Be?. In Proceedings of the 1st International Conference on Conversational User Interfaces (Dublin, Ireland) (CUI '19). Association for Computing Machinery, New York, NY, USA, Article 19, 3 pages. https://doi.org/10.1145/3342775.3342807 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Steven Guamán, Adrián Calvopiña, Pamela Orta, Freddy Tapia, and Sang Guun Yoo. 2018. Device Control System for a Smart Home Using Voice Commands: A Practical Case. In Proceedings of the 2018 10th International Conference on Information Management and Engineering (Salford, United Kingdom) (ICIME 2018). Association for Computing Machinery, New York, NY, USA, 86--89. https://doi.org/10.1145/3285957.3285977 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Nobukatsu Hojo, Yusuke Ijima, and Hideyuki Mizuno. 2018. DNN-based speech synthesis using speaker codes. IEICE TRANSACTIONS on Information and Systems 101, 2 (2018), 462--472.Google ScholarGoogle ScholarCross RefCross Ref
  7. Tomoyuki Kato, Jun Okamoto, and Makoto Shozakai. 2008. Analysis of drivers' speech in a car environment. In {INTERSPEECH} 2008, 9th Annual Conference of the International Speech Communication Association, Brisbane, Australia, September 22-26, 2008. ISCA, 1634--1637. http://www.isca-speech.org/archive/interspeech_2008/i08_1634.htmlGoogle ScholarGoogle Scholar
  8. T Le, P Gilberton, and N Q K Duong. 2019. Discriminate Natural versus Loudspeaker Emitted Speech. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers, 501--505. https://doi.org/10.1109/ICASSP.2019.8683227Google ScholarGoogle ScholarCross RefCross Ref
  9. Nadim Nachar. 2008. The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution. Tutorials in Quantitative Methods for Psychology 4 (03 2008). https://doi.org/10.20982/tqmp.04.1.p013Google ScholarGoogle Scholar
  10. Ryota Nishimura, Yuki Todo, Kazumasa Yamamoto, and Seiichi Nakagawa. 2013. Chat-like Spoken Dialog System for a Multi-party Dialog Incorporating Two Agents and a User. In Proc. of iHAI2013: The 1st International Conference on Human-Agent Interaction. II-2-p13.Google ScholarGoogle Scholar
  11. Helena Webb, Marina Jirotka, Alan F.T. Winfield, and Katie Winkle. 2019. Human-Robot Relationships and the Development of Responsible Social Robots. In Proceedings of the Halfway to the Future Symposium 2019 (HTTF 2019). Association for Computing Machinery, New York, NY, USA, 1--7. https://doi.org/10.1145/3363384.3363396 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K Yamamoto, K Inoue, S Nakamura, K Takanashi, and T Kawahara. 2018. Dialogue Behavior Control Model for Expressing a Character of Humanoid Robots. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). Institute of Electrical and Electronics Engineers, 1732--1737. https://doi.org/10.23919/APSIPA.2018.8659624Google ScholarGoogle ScholarCross RefCross Ref
  13. Yuichiro Yoshikawa, Takamasa Iio, Tsunehiro Arimoto, Hiroaki Sugiyama, and Hiroshi Ishiguro. 2017. Proactive Conversation between Multiple Robots to Improve the Sense of Human-Robot Conversation. In AAAI 2017 Fall Symposium Series. 288--294.Google ScholarGoogle Scholar

Index Terms

  1. Designing Personality Shifting Agent for Speech Recognition Failure

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            IVA '21: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents
            September 2021
            238 pages
            ISBN:9781450386197
            DOI:10.1145/3472306

            Copyright © 2021 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 14 September 2021

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

            Acceptance Rates

            Overall Acceptance Rate53of196submissions,27%

            Upcoming Conference

            IVA '24
            ACM International Conference on Intelligent Virtual Agents
            September 16 - 19, 2024
            GLASGOW , United Kingdom
          • Article Metrics

            • Downloads (Last 12 months)7
            • Downloads (Last 6 weeks)1

            Other Metrics

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader