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

Marek Kubis, Paweł Marek Skórzewski, Marcin Sowanski, TOMASZ ZIĘTKIEWICZ

Submission Type: Regular Long Paper

Submission Track: Dialogue and Interactive Systems

Submission Track 2: Speech and Multimodality

Keywords: ASR, NLU, Speech Recognition, TTS, Back Transcription, Evaluation, Robustness

TL;DR: This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models.

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.

Submission Number: 2984