Efficient Adapter Tuning Of Pre-trained Speech Models For Automatic Speaker Verification
2024 · Mufan Sang, John H. L. Hansen
Abstract
With excellent generalization ability, self-supervised speech models have shown impressive performance on various downstream speech tasks in the pre-training and fine-tuning paradigm. However, as the growing size of pre-trained models, fine-tuning becomes practically unfeasible due to heavy computation and storage overhead, as well as the risk of overfitting. Adapters are lightweight modules inserted into pre-trained models to facilitate parameter-efficient adaptation. In this paper, we propose an effective adapter framework designed for adapting self-supervised speech models to the speaker verification task. With a parallel adapter design, our proposed framework inserts two types of adapters into the pre-trained model, allowing the adaptation of latent features within intermediate Transformer layers and output embeddings from all Transformer layers. We conduct comprehensive experiments to validate the efficiency and effectiveness of the proposed framework. Experimental results on the
Authors
(none)
Tags
Stats
Related papers
- Parameter-efficient Transfer Learning Of Pre-trained Transformer Models For Speaker Verification Using Adapters (2022)0.00
- Unipet-spk: A Unified Framework For Parameter-efficient Tuning Of Pre-trained Speech Models For Robust Speaker Verification (2025)4.52
- Efficient Adapter Transfer Of Self-supervised Speech Models For Automatic Speech Recognition (2022)12.68
- Exploring Efficient-tuning Methods In Self-supervised Speech Models (2022)10.07
- Elp-adapters: Parameter Efficient Adapter Tuning For Various Speech Processing Tasks (2024)7.81
- An Adapter-based Unified Model For Multiple Spoken Language Processing Tasks (2024)0.00
- CHAPTER: Exploiting Convolutional Neural Network Adapters For Self-supervised Speech Models (2022)7.50
- SE/BN Adapter: Parametric Efficient Domain Adaptation For Speaker Recognition (2024)0.00