mlsanityData to 14 Oct 2026

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

arXiv 2606.15638 ↗First posted 14 Jun 2026eess.AS cs.SDcited by 0

On the map: State-space sequence models (S4, Mamba) → Speech and audio SSMs

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 30 of its 34 references that are in the map. First posted on arXiv 14 Jun 2026.

Builds on

30 of 34 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Mamba for Streaming ASR Combined with Unimodal Aggregation30 Sep
  2. 2024Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations4 Jun
  3. 2024Mamba in Speech: Towards an Alternative to Self-Attention21 May
  4. 2024Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation27 Mar

Other references · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  6. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  8. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  9. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  10. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  11. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
  12. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 872
14 more
  1. 2020AdapterFusion: Non-Destructive Task Composition for Transfer Learningcited by 788
  2. 2022AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognitioncited by 719
  3. 2021AST: Audio Spectrogram Transformercited by 643
  4. 2021Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworkscited by 284
  5. 2019Speech Model Pre-training for End-to-End Spoken Language Understandingcited by 193
  6. 2022Convolutional Bypasses Are Better Vision Transformer Adapterscited by 121
  7. 2024Open-Source Conversational AI with SpeechBrain 1.0cited by 56
  8. 2022Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognitioncited by 32
  9. 2021An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learningcited by 19
  10. 2023CL-MASR: A Continual Learning Benchmark for Multilingual ASRcited by 17
  11. 2022Parameter Efficient Transfer Learning for Various Speech Processing Taskscited by 9
  12. 2023Parameter-Efficient Transfer Learning of Audio Spectrogram Transformerscited by 8
  13. 2024Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapterscited by 5
  14. 2025The Curious Case of In-Training Compression of State Space Modelscited by 4

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  1. 2024Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis
  2. 2024Rethinking Mamba in Speech Processing by Self-Supervised Models
  3. 2025SAM: A Mamba-2 State-Space Audio-Language Model
  4. 2026MamTra: A Hybrid Mamba-Transformer Backbone for Speech Synthesis
  5. 2025An Exploration of Mamba for Speech Self-Supervised Models
  6. 2025TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model
  7. 2024Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models
  8. 2024Audio Mamba: Bidirectional State Space Model for Audio Representation Learning