An Exploration of Mamba for Speech Self-Supervised Models
Abstract
While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised learning (SSL) model remains underexplored, with prior studies limited to isolated tasks. To address this, we explore Mamba-based HuBERT models as alternatives to Transformer-based SSL architectures. Leveraging the linear-time Selective State Space, these models enable fine-tuning on long-context ASR with significantly lower compute. Moreover, they show superior performance when fine-tuned for streaming ASR. Beyond fine-tuning, these models show competitive performance on SUPERB probing benchmarks, particularly in causal settings. Our analysis shows that they yield higher-quality quantized representations and capture speaker-related features more distinctly than Transformer-based models. These findings highlight Mamba-based SSL as a promising and complementary direction for long-sequence modeling, real-time speech modeling, and speech unit extraction. The codebase is available at https://github.com/hckuo145/Mamba-based-HuBERT.
Cite
@article{arxiv.2506.12606,
title = {An Exploration of Mamba for Speech Self-Supervised Models},
author = {Tzu-Quan Lin and Heng-Cheng Kuo and Tzu-Chieh Wei and Hsi-Chun Cheng and Chun Wei Chen and Hsien-Fu Hsiao and Yu Tsao and Hung-yi Lee},
journal= {arXiv preprint arXiv:2506.12606},
year = {2026}
}
Comments
Accepted at ACL 2026 Main Conference