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Conference papers

ASR error management for improving spoken language understanding

Abstract : This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .
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Contributor : Yannick Estève Connect in order to contact the contributor
Submitted on : Tuesday, May 23, 2017 - 1:05:03 AM
Last modification on : Thursday, June 18, 2020 - 12:58:24 PM
Long-term archiving on: : Friday, August 25, 2017 - 1:00:00 AM


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  • HAL Id : hal-01526298, version 1
  • ARXIV : 1705.09515



Edwin Simonnet, Sahar Ghannay, Nathalie Camelin, Yannick Estève, Renato de Mori. ASR error management for improving spoken language understanding. Interspeech 2017, Aug 2017, Stockholm, Sweden. ⟨hal-01526298⟩



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