Error Analysis Applied to End-to-End Spoken Language Understanding
Antoine Caubrière, Sahar Ghannay, Natalia A. Tomashenko, Renato De Mori, Antoine Laurent, Emmanuel Morin, Yannick Estève
Abstract
This paper presents a qualitative study of errors produced by an end-to-end spoken language understanding (SLU) system (speech signal to concepts) that reaches state of the art performance. Different studies are proposed to better understand the weaknesses of such systems: comparison to a classical pipeline SLU system, a study on the cause of concept deletions (the most frequent error), observation of a problem in the capability of the end-to-end SLU system to segment correctly concepts, analysis of the system behavior to process unseen concept/value pairs, analysis of the benefit of the curriculum-based transfer learning approach. Last, we proposed a way to compute embeddings of sub-sequences that seem to contain relevant information for future work.
BibTeX
@inproceedings{icassp2020_erroranalysisapp,
title = {Error Analysis Applied to End-to-End Spoken Language Understanding},
author = {Antoine Caubrière and Sahar Ghannay and Natalia A. Tomashenko and Renato De Mori and Antoine Laurent and Emmanuel Morin and Yannick Estève},
booktitle = {ICASSP 2020},
year = {2020}
}