Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding

Mohan Li; Rama Doddipatla

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arXiv · 2304.10869

Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding

Mohan Li·Rama Doddipatla

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Abstract

This paper presents the use of non-autoregressive (NAR) approaches for joint automatic speech recognition (ASR) and spoken language understanding (SLU) tasks. The proposed NAR systems employ a Conformer encoder that applies connectionist temporal classification (CTC) to transcribe the speech utterance into raw ASR hypotheses, which are further refined with a bidirectional encoder representations from Transformers (BERT)-like decoder. In the meantime, the intent and slot labels of the utterance are predicted simultaneously using the same decoder. Both Mask-CTC and self-conditioned CTC (SC-CTC) approaches are explored for this study. Experiments conducted on the SLURP dataset show that the proposed SC-Mask-CTC NAR system achieves 3.7% and 3.2% absolute gains in SLU metrics and a competitive level of ASR accuracy, when compared to a Conformer-Transformer based autoregressive (AR) model. Additionally, the NAR systems achieve 6x faster decoding speed than the AR baseline.

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BibTeX RIS

Mohan Li, Rama Doddipatla. 2023-04-21. Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding. https://arxiv.org/abs/2304.10869

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