  This paper explores the capability of Mamba, a recently proposed architecture
based on state space models (SSMs), as a competitive alternative to
Transformer-based models. In the speech domain, well-designed Transformer-based
models, such as the Conformer and E-Branchformer, have become the de facto
standards. Extensive evaluations have demonstrated the effectiveness of these
Transformer-based models across a wide range of speech tasks. In contrast, the
evaluation of SSMs has been limited to a few tasks, such as automatic speech
recognition (ASR) and speech synthesis. In this paper, we compared Mamba with
state-of-the-art Transformer variants for various speech applications,
including ASR, text-to-speech, spoken language understanding, and speech
summarization. Experimental evaluations revealed that Mamba achieves comparable
or better performance than Transformer-based models, and demonstrated its
efficiency in long-form speech processing.
