标题： 用于在线语音识别的结构化状态空间序列模型增强的构象器 显示英文标题

标题： Augmenting conformers with structured state-space sequence models for online speech recognition

摘要： 在线语音识别，其中模型仅访问左侧的上下文，是ASR系统的重要且具有挑战性的用例。 在本工作中，我们通过结合结构化状态空间序列模型（S4），研究了增强神经编码器用于在线ASR的方法，这是一类提供参数高效方式以访问任意长左侧上下文的模型。 我们进行了系统的消融研究，以比较S4模型的不同变体，并提出了两种将它们与卷积结合的新方法。 我们发现，最有效的设计是堆叠一个使用实数值递归权重的小型S4与局部卷积，使它们能够互补工作。 我们的最佳模型在Librispeech测试集上的词错误率（WER）达到了4.01%/8.53%，超过了经过广泛调优的Conformer模型。 显示英文摘要

摘要： Online speech recognition, where the model only accesses context to the left, is an important and challenging use case for ASR systems. In this work, we investigate augmenting neural encoders for online ASR by incorporating structured state-space sequence models (S4), a family of models that provide a parameter-efficient way of accessing arbitrarily long left context. We performed systematic ablation studies to compare variants of S4 models and propose two novel approaches that combine them with convolutions. We found that the most effective design is to stack a small S4 using real-valued recurrent weights with a local convolution, allowing them to work complementarily. Our best model achieves WERs of 4.01%/8.53% on test sets from Librispeech, outperforming Conformers with extensively tuned convolution.