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.

翻译：本文提出使用非自回归（NAR）方法处理联合自动语音识别（ASR）及口语理解（SLU）任务。所提出的NAR系统采用Conformer编码器，通过连接时序分类（CTC）将语音转录为原始ASR假设，并利用类似BERT（基于Transformer的双向编码器表示）的解码器对这些假设进行进一步优化。同时，同一解码器可并行预测话语的意图与槽位标签。本研究探索了Mask-CTC和自条件CTC（SC-CTC）两种方法。在SLURP数据集上的实验表明，与基于Conformer-Transformer的自回归（AR）模型相比，所提出的SC-Mask-CTC NAR系统在SLU指标上取得3.7%和3.2%的绝对提升，并达到具有竞争力的ASR精度。此外，NAR系统的解码速度比AR基线提升6倍。