Discovering Undiscovered States in Human Robot Verbal Interaction
Pages 1077 - 1079
Abstract
Despite the abilities of automatic speech recognition systems such as CMU Sphinx, Google Speech-to-Text API, and Amazon Tran- scribe to recognize a variety of voices, they often face challenges in accurately processing complete information. To overcome this limitation, we propose a novel approach utilizing Markov Decision Processes. Our research involves an intelligent agent that evaluates human speech (n=1) and identifies new states through learning, enabling it to process more comprehensive information compared to traditional systems. The paper illustrates two scenarios : one where the intelligent agent explores by detecting undiscovered states and ultimately reaches the goal state, and another where while discovering new states it also revisits the previous states.
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References
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Published In
March 2024
1408 pages
ISBN:9798400703232
DOI:10.1145/3610978
- General Chairs:
- Dan Grollman,
- Elizabeth Broadbent,
- Program Chairs:
- Wendy Ju,
- Harold Soh,
- Tom Williams
Copyright � 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 March 2024
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- Short-paper
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- Purdue Northwest Catalyst Grant
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HRI '24: ACM/IEEE International Conference on Human-Robot Interaction
March 11 - 15, 2024
CO, Boulder, USA
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Overall Acceptance Rate 268 of 1,124 submissions, 24%
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