A SYSTEMATIC REVIEW OF PARAMETER-EFFICIENT FINE-TUNING (PEFT) IN SPEECH PROCESSING

Abstract

Recent breakthroughs in large-scale speech models such as Whisper, Wav2Vec 2.0, and HuBERT have greatly enhanced speech processing tasks. Full fine-tuning comes at a prohibitive cost, though, which restricts their application to low- resource or real-time settings. The parameter-efficient fine-tuning (PEFT) approaches—e.g., LoRA, QLoRA, adapters, and prompt tuning—allow for compact adaptation by fine-tuning only a small subset of parameters. We review 33 studies (2021–2025) using PEFT for applications such as ASR, speaker verification, and emotion recognition. We organize methods by task, compare efficiency and accuracy, and determine prominent trends. Results indicate PEFT produces competitive results with reduced cost, enabling scalable deployment in resource-poor environments.