MambAdapter: Lightweight Mamba-Based Adapters for Parameter-Efficient Transfer Learning in Speech and Audio

2026-06-14

AI Summary

This work addresses the high computational and memory costs associated with transfer learning in speech and audio foundation models by introducing, for the first time, the Mamba state space model into a parameter-efficient transfer learning (PETL) framework. The authors propose a lightweight method based on low-rank bottleneck adapters, embedding a shared-parameter Mamba module within the adapter architecture to substantially reduce the number of trainable parameters while enhancing audio feature modeling capacity. Experimental results demonstrate that the proposed approach achieves competitive or superior performance compared to existing PETL methods across four audio classification benchmarks and speech recognition tasks spanning five languages—even under stricter parameter budgets.

Abstract

Fine-tuning Transformer-based foundation models has become the dominant strategy for domain adaptation in audio and speech processing. To reduce the computational and memory costs of this process, parameter-efficient transfer learning (PETL) methods have been widely explored. Meanwhile, Mamba, a recent state-space model, has emerged as a promising alternative to Transformers for sequence modeling. In this work, we present MambAdapter, a parameter-efficient transfer learning approach that integrates Mamba into low-rank bottleneck adapters. Our design combines parameter sharing across adapters with the injection of a lightweight Mamba module, enabling more effective modeling of audio features. We demonstrate that MambAdapter matches or outperforms strong PETL baselines on four audio classification tasks and five speech recognition languages, even when operating under reduced parameter budgets.

Problem

• parameter-efficient transfer learning
• speech processing
• audio classification
• domain adaptation
• foundation models

Innovation

• Mamba
• parameter-efficient transfer learning
• audio processing
• state-space model
• adapter