Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
Computation and Language
2023-10-26 v1 Artificial Intelligence
Sound
Audio and Speech Processing
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.
Keywords
Cite
@article{arxiv.2310.16609,
title = {Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors},
author = {Marek Kubis and Paweł Skórzewski and Marcin Sowański and Tomasz Ziętkiewicz},
journal= {arXiv preprint arXiv:2310.16609},
year = {2023}
}
Comments
Accepted to EMNLP 2023 main conference