The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Abstract
Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content. In this paper, we propose a new listening comprehension task - Spoken SQuAD. On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.
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Prior datasets investigated were restricted to only failures at the understanding and response level or had very specific context that did not capture the breadth of possible failures users experience.
“In these cases, we found that the datasets were still restricted to only failures at the understanding and response level [26, 32] or the context for the failures was very specific and did not necessarily capture the breadth of possible failures users experience [1, 2].”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [32]
Speech recognition errors have catastrophic impact on machine comprehension.
“Lee et al. [32] showed speech recognition errors have catastrophic impact on machine comprehension.”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [32]
Citations
Cites
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
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A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures
Baughan, A., Wang, X., Liu, A., Mercurio, A., Chen, J., & Ma, X. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581152 · Cited by 32
“The NLP community has also examined voice assistant failures from a slightly diferent angle, focusing on the robustness of diferent NLP components underlying voice assistants, such as models for tasks in natural language inference [44], question answering [26, 42], and speech recognition [32].”
BackgroundMethodInfluentialInvestigating the Effects of Word Substitution Errors on Sentence Embeddings
Voleti, R., Liss, J. M., & Berisha, V. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683367 · Cited by 17
Metadata
- Authors
- Lee, C.-H., Wu, S.-L., Liu, C.-L., & Lee, H.-Y.
- Venue
- Interspeech 2018
- Publisher
- ISCA
- Published
- 2018-09-02
- Pages
- 3459-3463
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2962854302
- Cited by
- 45
- References
- 1
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 7 · 2021: 10 · 2020: 3 · 2019: 5 · 2018: 2
- FWCI
- 2.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- listening comprehension, speech recognition errors, question answering, error mitigation, machine reading comprehension, spoken question answering
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