Text-only adaptation in LLM-based ASR through text denoising
Abstract
Adapting automatic speech recognition (ASR) systems based on large language models (LLMs) to new domains using text-only data is a significant yet underexplored challenge. Standard fine-tuning of the LLM on target-domain text often disrupts the critical alignment between speech and text modalities learned by the projector, degrading performance. We introduce a novel text-only adaptation method that emulates the audio projection task by treating it as a text denoising task. Our approach thus trains the LLM to recover clean transcripts from noisy inputs. This process effectively adapts the model to a target domain while preserving cross-modal alignment. Our solution is lightweight, requiring no architectural changes or additional parameters. Extensive evaluation on two datasets demonstrates up to 22.1% relative improvement, outperforming recent state-of-the-art text-only adaptation methods.
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 main contribution of this paper is the introduction of a novel text-only adaptation method for LLM-based ASR, which effectively preserves cross-modal alignment while improving performance in various adaptation scenarios. This work represents a meaningful advancement in the field, addressing a critical challenge in ASR systems and providing a practical solution for domain adaptation.
Methodology
The proposed methodology introduces a novel approach to text-only adaptation in LLM-based ASR by framing the adaptation as a text denoising task. This reframing is innovative as it allows the model to learn from noisy text inputs without requiring additional parameters or architectural changes. The use of a multi-view noise-driven batching strategy is particularly effective in maintaining the alignment between speech and text modalities, which is a critical aspect of ASR systems. The authors provide a clear explanation of how the noise function is constructed and how it facilitates the training process, making the methodology both sound and theoretically grounded.
Results & evaluation
The experimental evaluation is thorough, utilizing two distinct datasets that represent realistic conversational scenarios. The results demonstrate significant improvements in WER across various adaptation scenarios, showcasing the effectiveness of the proposed method compared to existing techniques. The inclusion of ablation studies further strengthens the findings by isolating the contributions of key components in the training process. However, the paper could benefit from additional details on the experimental setup, such as hyperparameter tuning and validation strategies.
Reproducibility
The paper provides a solid foundation for reproducibility with a detailed description of the experimental setup, including the models used and the training process. However, the lack of publicly available code or datasets limits the ability for other researchers to fully replicate the results. Including a link to a GitHub repository or similar would enhance the reproducibility of the findings.
Limitations
One limitation of the proposed approach is its reliance on the quality of the noise function, which may not perfectly emulate the outputs of a speech projector. Additionally, while the method shows promise in various adaptation scenarios, performance still lags behind audio-based adaptation, indicating that further improvements are needed. The paper also does not address the potential computational costs associated with the proposed training strategy.
Broader impact
This research has significant implications for the field of ASR, particularly in scenarios where audio data is scarce or expensive to obtain. By enabling effective adaptation using only text data, the proposed method could facilitate the deployment of ASR systems in diverse domains, enhancing accessibility and usability in real-world applications. The approach could also inspire further research into alternative adaptation strategies that leverage text data in innovative ways. The main contribution of this paper is the introduction of a novel text-only adaptation method for LLM-based ASR, which effectively preserves cross-modal alignment while improving performance in various adaptation scenarios. This work represents a meaningful advancement in the field, addressing a critical challenge in ASR systems and providing a practical solution for domain adaptation.
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