Exploring the Capability of Mamba in Speech Applications
<|reference_start|>Exploring the Capability of Mamba in Speech Applications: 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.<|reference_end|>
@article{miyazaki2024exploring,
    title={Exploring the Capability of Mamba in Speech Applications},
    author={Koichi Miyazaki, Yoshiki Masuyama, Masato Murata},
    journal={arXiv preprint arXiv:2406.16808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16808},
    primaryClass={cs.SD eess.AS}
}