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        <title>Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors</title>
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        <namePart type="given">Marek</namePart>
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        <namePart type="given">Paweł</namePart>
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    <name type="personal">
        <namePart type="given">Tomasz</namePart>
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            <namePart type="given">Houda</namePart>
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.724</identifier>
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%0 Conference Proceedings
%T Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
%A Kubis, Marek
%A Skórzewski, Paweł
%A Sowański, Marcin
%A Zietkiewicz, Tomasz
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kubis-etal-2023-back
%X 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.
%R 10.18653/v1/2023.emnlp-main.724
%U https://aclanthology.org/2023.emnlp-main.724
%U https://doi.org/10.18653/v1/2023.emnlp-main.724
%P 11824-11835

Markdown (Informal)

[Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors](https://aclanthology.org/2023.emnlp-main.724) (Kubis et al., EMNLP 2023)

• Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors (Kubis et al., EMNLP 2023)

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.