Abstract
This paper presents a system that automates activation of events that improve the accessibility and enhance the experience in theatrical performances in real time and proposes and evaluates the core method employed therein. This method aligns a given set of subtitles that is created and synchronized by experts for a given “rehearsal” audio stream, to a new “performance” audio stream in real-time. The performance stream may be different from the rehearsal not only in terms of timing, but also in terms of spoken content. The method is built around an Automatic Speech Recognition System (ASR) that captures the performance stream in real-time and generates suggestions for subtitle timing corrections. The system is evaluated in an experimental performance with 35 participants while the core method is analysed independently, using sixteen artificial rehearsal-performance pair scenarios. Objective and subjective evaluation reveals specific directions for improvements for the core method. The investigation of the system shows that this innovative approach is promising for improving accessibility and the enhancement of the theatrical experience of audiences.
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Notes
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The interim results keep growing in length as long as new audio is recorded by the microphone sensors that are monitored by the ASR agent. A final result only appears after the service does not detect any audio input and subsequently the upcoming transcriptions are reset in terms of length.
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Acknowledgments
This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (NLP-Theatre, project code: T1EDK-00508).
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Katsalis, A. et al. (2023). NLP-Theatre: Employing Speech Recognition Technologies for Improving Accessibility and Augmenting the Theatrical Experience. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-031-16078-3_35
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