标题： SSVD：用于语音识别中领域偏移下的结构化奇异值分解参数高效微调和基准测试 显示英文标题

标题： SSVD: Structured SVD for Parameter-Efficient Fine-Tuning and Benchmarking under Domain Shift in ASR

摘要： 参数高效微调（PEFT）已成为适应大型基础模型的可扩展解决方案。 虽然低秩微调（LoRA）在语音应用中被广泛使用，但其最先进的变体，例如VeRA、DoRA、PiSSA和SVFT，主要是为语言和视觉任务开发的，在语音领域验证有限。 这项工作首次在ESPnet中全面整合并基准测试了这些PEFT方法。 我们进一步引入了结构化奇异值分解引导（SSVD）微调，该方法选择性地旋转与输入相关的右奇异向量，同时保持与输出相关的向量固定，以保留语义映射。 这种设计能够在少量可训练参数的情况下实现稳健的领域适应，并提高效率。 我们在从0.1B到2B的不同模型规模上，对领域转移的语音识别任务进行了所有方法的评估，包括儿童语音和方言变化。 所有实现都已在ESPnet中发布，以支持可重复性和未来工作。 显示英文摘要

摘要： Parameter-efficient fine-tuning (PEFT) has emerged as a scalable solution for adapting large foundation models. While low-rank adaptation (LoRA) is widely used in speech applications, its state-of-the-art variants, e.g., VeRA, DoRA, PiSSA, and SVFT, are developed mainly for language and vision tasks, with limited validation in speech. This work presents the first comprehensive integration and benchmarking of these PEFT methods within ESPnet. We further introduce structured SVD-guided (SSVD) fine-tuning, which selectively rotates input-associated right singular vectors while keeping output-associated vectors fixed to preserve semantic mappings. This design enables robust domain adaptation with minimal trainable parameters and improved efficiency. We evaluate all methods on domain-shifted speech recognition tasks, including child speech and dialectal variation, across model scales from 0.1B to 2B. All implementations are released in ESPnet to support reproducibility and future work.