Automatic Lecture Subtitle Generation and How It Helps
Abstract
In this paper we propose an integrated framework of automatic bilingual subtitle generation for lecture videos, especially for MOOCs. The framework consists of Automatic Speech Recognition (ASR), Sentence Boundary Detection (SBD), and Machine Translation (MT). Then we quantitatively evaluate the auto-generated subtitles, the manually produced subtitles from scratch, and the auto-generated subtitles with manual modification in term of accuracy and time expenditure, in both original and target languages. The result shows that the auto-generated subtitles in the original language (English) are fairly accurate already. By using them as the draft, human subtitle producers can save 54% of the working time and simultaneously reduce the error rate by 54.3%, which is a significant improvement. However, the effectiveness of machine translated subtitles (English to Chinese) is limited. In the end, if the proposed framework is applied, the total working time in preparing bilingual subtitles can be shortened by approximately 1/3, with no decline in quality.
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Previous research suggests using ASR as a basis for manual corrections to create more accurate transcripts compared to a fully manual approach.
“Previous research suggests using ASR as a basis for manual corrections to create more accurate transcripts compared to a fully manual approach [Che et al. 2017].”
From Measuring the Accuracy of Automatic Speech Recognition Solutions (2023) · cites this paper as Che et al. 2017
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Measuring the Accuracy of Automatic Speech Recognition Solutions
Kuhn, K., Kersken, V., Reuter, B., Egger, N., & Zimmermann, G. (2023) · ACM Transactions on Accessible Computing · doi:10.1145/3636513 · Cited by 44 · see citing works
“Otherwise we implemented the HTTP-or WebSocket-API according to the vendors’ documentation.”
Method
- Authors
- Che, X., Luo, S., Yang, H., & Meinel, C.
- Affiliations
- Hasso Plattner Institute (DE)
- Venue
- 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 34-38
- OpenAlex
- W2744215503
- Cited by
- 29
- References
- 0
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 8 · 2022: 1 · 2021: 4 · 2019: 3 · 2018: 1
- FWCI
- 12.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Advanced Learning Technologies (ICALT) (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › Subtitles and Audiovisual Media
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- video lectures, MOOCs, bilingual subtitles, speech recognition, sentence segmentation, machine translation, English-Chinese translation
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