Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models
<|reference_start|>Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models: Current automatic speech recognition systems struggle with modeling long speech sequences due to high quadratic complexity of Transformer-based models. Selective state space models such as Mamba has performed well on long-sequence modeling in natural language processing and computer vision tasks. However, research endeavors in speech technology tasks has been under-explored. We propose Speech-Mamba, which incorporates selective state space modeling in Transformer neural architectures. Long sequence representations with selective state space models in Speech-Mamba is complemented with lower-level representations from Transformer-based modeling. Speech-mamba achieves better capacity to model long-range dependencies, as it scales near-linearly with sequence length.<|reference_end|>
@article{gao2024speech-mamba:,
    title={Speech-Mamba: Long-Context Speech Recognition with Selective State
  Spaces Models},
    author={Xiaoxue Gao, Nancy F. Chen},
    journal={arXiv preprint arXiv:2409.18654},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18654},
    primaryClass={eess.AS cs.SD}
}