State space models (SSMs) have recently shown promising results on
small-scale sequence and language modelling tasks, rivalling and outperforming
many attention-based approaches. In this paper, we propose a multi-head state
space (MH-SSM) architecture equipped with special gating mechanisms, where
parallel heads are taught to learn local and global temporal dynamics on
sequence data. As a drop-in replacement for multi-head attention in transformer
encoders, this new model significantly outperforms the transformer transducer
on the LibriSpeech speech recognition corpus. Furthermore, we augment the
transformer block with MH-SSMs layers, referred to as the Stateformer,
achieving state-of-the-art performance on the LibriSpeech task, with word error
rates of 1.76\%/4.37\% on the development and 1.91\%/4.36\% on the test sets
without using an external language model.

本文提出了一种带有特殊门控机制的多头状态空间（MH-SSM）架构，它可以作为多头注意力在转换器编码器中的替代品，在对于序列数据的处理中能够比转换器转录器更好地优化带来的结果，在 LibriSpeech 语音识别数据集上提高了性能。同时，我们将 MH-SSMs 层与转换器块相结合，成为 Stateformer，且无需使用外部语言模型，在 LibriSpeech 任务中实现了最先进的性能，其开发和测试集上的字错率分别为 1.76％/4.37％和 1.91％/4.36％。