An Exploration of Mamba for Speech Self-Supervised Models

• URL: http://arxiv.org/abs/2506.12606v1
• Date: Sat, 14 Jun 2025 19:00:44 GMT
• Title: An Exploration of Mamba for Speech Self-Supervised Models
• Authors: Tzu-Quan Lin, Heng-Cheng Kuo, Tzu-Chieh Wei, Hsi-Chun Cheng, Chun-Wei Chen, Hsien-Fu Hsiao, Yu Tsao, Hung-yi Lee,
• Abstract summary: We explore Mamba-based HuBERT models as alternatives to Transformer-based SSL architectures.<n>HuBERT models enable fine-tuning on long-context ASR with significantly lower compute.<n>These findings highlight Mamba-based SSL as a promising and complementary direction for long-sequence modeling, real-time speech modeling, and speech unit extraction.
• Score: 48.01992287080999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised (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.

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