# Exploring Efficient-tuning Methods in Self-supervised Speech Models

arXiv: https://arxiv.org/abs/2210.06175 · HTML: https://mlsanity.com/paper/2210.06175/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 24 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://mlsanity.com/paper/2106.03164/) (2021)
- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://mlsanity.com/paper/2106.09296/) (2021)
- [DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization](https://mlsanity.com/paper/2012.06659/) (2020)
- [Counter-Interference Adapter for Multilingual Machine Translation](https://mlsanity.com/paper/2104.08154/) (2021)
- [Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition](https://mlsanity.com/paper/2202.03218/) (2022)
- [AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks](https://mlsanity.com/paper/2205.00305/) (2022)
- [DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR](https://mlsanity.com/paper/2206.07931/) (2022)
- [An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2107.13530/) (2021)
- [SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks](https://mlsanity.com/paper/2203.16773/) (2022)

## Built on by (most cited first, 26 of 26)

- [Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech](https://mlsanity.com/paper/2309.09510/) (2023)
- [Modular Deep Learning](https://mlsanity.com/paper/2302.11529/) (2023)
- [Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition](https://mlsanity.com/paper/2310.06434/) (2023)
- [Learning from models beyond fine-tuning](https://mlsanity.com/paper/2310.08184/) (2023)
- [ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets](https://mlsanity.com/paper/2406.08641/) (2024)
- [SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2210.08634/) (2022)
- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models](https://mlsanity.com/paper/2212.01282/) (2022)
- [Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition](https://mlsanity.com/paper/2309.15223/) (2023)
- [How to Estimate Model Transferability of Pre-Trained Speech Models?](https://mlsanity.com/paper/2306.01015/) (2023)
- [How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario](https://mlsanity.com/paper/2411.18217/) (2024)
- [Adapting Whisper for Parameter-efficient Code-Switching Speech Recognition via Soft Prompt Tuning](https://mlsanity.com/paper/2506.21576/) (2025)
- [MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task Finetuning](https://mlsanity.com/paper/2310.09853/) (2023)
- [Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters](https://mlsanity.com/paper/2401.05111/) (2024)
- [Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model](https://mlsanity.com/paper/2310.02971/) (2023)
- [PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques](https://mlsanity.com/paper/2401.02122/) (2024)
- [Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation](https://mlsanity.com/paper/2402.11747/) (2024)
- [ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks](https://mlsanity.com/paper/2407.21066/) (2024)
- [Lightweight Zero-shot Text-to-Speech with Mixture of Adapters](https://mlsanity.com/paper/2407.01291/) (2024)
- [CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing](https://mlsanity.com/paper/2412.04425/) (2024)
- [Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization](https://mlsanity.com/paper/2502.12672/) (2025)
- [Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR](https://mlsanity.com/paper/2406.07842/) (2024)
- [JOOCI: a Framework for Learning Comprehensive Speech Representations](https://mlsanity.com/paper/2410.11086/) (2024)
- [BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot Captioning](https://mlsanity.com/paper/2309.14774/) (2023)
- [On-the-fly Routing for Zero-shot MoE Speaker Adaptation of Speech Foundation Models for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.22072/) (2025)
- [Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean](https://mlsanity.com/paper/2606.26618/) (2026)

## Abstract (arXiv)

In this study, we aim to explore efficient tuning methods for speech self-supervised learning. Recent studies show that self-supervised learning (SSL) can learn powerful representations for different speech tasks. However, fine-tuning pre-trained models for each downstream task is parameter-inefficient since SSL models are notoriously large with millions of parameters. Adapters are lightweight modules commonly used in NLP to solve this problem. In downstream tasks, the parameters of SSL models are frozen, and only the adapters are trained. Given the lack of studies generally exploring the effectiveness of adapters for self-supervised speech tasks, we intend to fill this gap by adding various adapter modules in pre-trained speech SSL models. We show that the performance parity can be achieved with over 90% parameter reduction, and discussed the pros and cons of efficient tuning techniques. This is the first comprehensive investigation of various adapter types across speech tasks.