Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.18654 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909327837626368 |
|---|---|
| author | Gao, Xiaoxue Chen, Nancy F. |
| author_facet | Gao, Xiaoxue Chen, Nancy F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18654 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models Gao, Xiaoxue Chen, Nancy F. Audio and Speech Processing Sound 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. |
| title | Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.18654 |