SSVD-O: Parameter-Efficient Fine-Tuning with Structured SVD for Speech Recognition
Abstract
Parameter-efficient fine-tuning (PEFT) is a scalable approach for adapting large speech foundation models to new domains. While methods such as LoRA and its state-of-the-art variants reduce adaptation costs, they typically allocate parameters uniformly across model subspaces, which limits their efficiency and scalability in speech applications. Building on our prior work, this paper introduces SSVD-Outer (SSVD-O), an extension of the structured SVD-guided (SSVD) fine-tuning method. SSVD-O combines input acoustic feature space-associated inner transformations with output semantic feature space-associated outer transformations to enable scalable and balanced adaptation. We conduct the first systematic analysis of parameter budget allocation across model subspaces in PEFT for automatic speech recognition (ASR), and investigate the trade-off between learning and forgetting under constrained resources. SSVD-O is benchmarked against LoRA, DoRA, PiSSA, and SSVD on domain-shifted ASR tasks, including child speech and regional accents, across model scales from 0.1B to 2B within the ESPnet framework. Experimental results show that SSVD-O consistently narrows the performance gap to full fine-tuning while improving generalization and mitigating catastrophic forgetting.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces SSVD-O, a novel structured SVD-guided PEFT method that enhances adaptation efficiency in speech recognition tasks. The comprehensive analysis of its methodology, experimental validation, and implications for the field underscores its potential to advance the state-of-the-art in automatic speech recognition.
Methodology
The proposed SSVD-O method innovatively combines inner and outer transformations to enhance parameter-efficient fine-tuning (PEFT) for speech recognition tasks. By leveraging structured singular value decomposition (SVD), the authors effectively address the limitations of existing methods like LoRA, DoRA, and PiSSA, particularly in the context of domain-shifted automatic speech recognition (ASR). The systematic analysis of parameter budget allocation across model subspaces is a significant methodological contribution, providing insights into the trade-offs between learning and forgetting.
Results & evaluation
The experiments are well-structured, benchmarking SSVD-O against several state-of-the-art PEFT methods across various model scales and domain-shifted tasks. The use of diverse datasets, including child speech and regional accents, strengthens the evaluation. The results demonstrate that SSVD-O outperforms existing methods and narrows the performance gap to full fine-tuning, showcasing its effectiveness in real-world applications.
Reproducibility
The paper mentions the use of the ESPnet framework for implementation, which is a widely recognized toolkit in the speech processing community. However, the lack of specific URLs for code or datasets limits reproducibility. Detailed descriptions of the experimental setup and parameter configurations are provided, which aids in replicating the results.
Limitations
One limitation is the potential complexity introduced by the dual transformation approach, which may require careful tuning of parameters. Additionally, while the paper addresses catastrophic forgetting, it does not explore the long-term implications of using SSVD-O in continual learning scenarios extensively. The focus on specific ASR tasks may also limit the generalizability of the findings to other domains.
Broader impact
The advancements presented in this paper have significant implications for the field of speech recognition, particularly in adapting large models to low-resource and domain-specific tasks. The methodology could be applied to various applications, including educational tools for children, assistive technologies for individuals with speech disorders, and regional dialect recognition systems. The findings promote more efficient use of computational resources in deploying large-scale models in practical settings. The paper introduces SSVD-O, a novel structured SVD-guided PEFT method that enhances adaptation efficiency in speech recognition tasks. The comprehensive analysis of its methodology, experimental validation, and implications for the field underscores its potential to advance the state-of-the-art in automatic speech recognition.
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