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Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors

Marek Kubis, Paweł Skórzewski, Marcin Sowański, Tomasz Zietkiewicz


Abstract
In a spoken dialogue system, an NLU model is preceded by a speech recognition system that can deteriorate the performance of natural language understanding. This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models. The proposed method combines the back transcription procedure with a fine-grained technique for categorizing the errors that affect the performance of NLU models. The method relies on the usage of synthesized speech for NLU evaluation. We show that the use of synthesized speech in place of audio recording does not change the outcomes of the presented technique in a significant way.
Anthology ID:
2023.emnlp-main.724
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11824–11835
Language:
URL:
https://aclanthology.org/2023.emnlp-main.724
DOI:
10.18653/v1/2023.emnlp-main.724
Bibkey:
Cite (ACL):
Marek Kubis, Paweł Skórzewski, Marcin Sowański, and Tomasz Zietkiewicz. 2023. Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11824–11835, Singapore. Association for Computational Linguistics.
Cite (Informal):
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors (Kubis et al., EMNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.emnlp-main.724.pdf
Video:
 https://aclanthology.org/2023.emnlp-main.724.mp4