Refining Pseudo-Audio Prompts with Speech-Text Alignment for Text-Only Domain Adaptation in LLM-Based ASR
Abstract
LLM-based automatic speech recognition models demonstrate strong performance by connecting audio encoders and LLMs. However, data scarcity of paired speech and transcription often hinders their adaptation to new domains, making text-only domain adaptation crucial. Existing methods typically rely on either fine-tuning the LLM alone or employing pseudo-audio prompts. The former neglects essential acoustic context, while the latter either suffers from limited scalability in data-scarce conditions, or yields inexpressive prompts by leveraging only textual features, ignoring audio modality. To address this, we propose an enhanced framework that explicitly models speech-text alignment. Our method efficiently generates highly expressive pseudo-audio prompts that bridges the modality gap, enabling effective target-domain adaptation. Experiments demonstrate that our approach outperforms existing text-only methods, improving both overall error rates and out-of-vocabulary coverage.
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 the TE2SL framework, which enhances text-only domain adaptation in LLM-based ASR by generating expressive pseudo-audio prompts through a learnable refinement module. This work represents a significant advancement in bridging the modality gap in ASR systems, with promising implications for improving performance in data-scarce environments.
Methodology
The proposed Text-Embedding-to-Speech-Latent (TE2SL) framework innovatively addresses the challenge of text-only domain adaptation in LLM-based ASR by introducing a learnable refinement module that enhances the quality of pseudo-audio prompts. This method effectively bridges the modality gap by ensuring that the synthesized prompts are both sample-dependent and aligned with the characteristics of the audio encoder and projector. The methodology is well-structured, with a clear distinction between training and adaptation phases, and utilizes a Conformer architecture to achieve this refinement. The focus on architecture-aware synthesis is a significant advancement over previous heuristic approaches.
Results & evaluation
The experiments conducted are thorough, comparing the TE2SL framework against established baselines, including LLM-only fine-tuning and pseudo-audio prompt methods. The results demonstrate substantial improvements in both recognition accuracy and out-of-vocabulary (OOV) recall across multiple datasets in English and Japanese, validating the effectiveness of the proposed method. The use of diverse datasets strengthens the generalizability of the findings, and the metrics employed (WER and CER) are appropriate for evaluating ASR performance.
Reproducibility
The paper provides a detailed description of the experimental setup, including model architectures, training configurations, and evaluation metrics. However, the absence of a publicly available code repository or demo limits the reproducibility of the results. Clearer documentation or a supplementary material section with implementation details could enhance reproducibility.
Limitations
One limitation is the reliance on the quality of the audio encoder and projector, which may vary across different languages or domains. Additionally, while the method shows promise in improving OOV recall, the paper does not extensively discuss the implications of these improvements in practical applications. The scalability of the TE2SL framework in low-resource settings, where high-quality audio encoders may not be available, also warrants further exploration.
Broader impact
The proposed approach has significant potential applications in various domains where ASR systems are deployed, particularly in low-resource languages or specialized fields with limited paired data. By improving domain adaptation capabilities, this work can enhance accessibility and usability of ASR technologies in diverse linguistic contexts. The findings could also inform future research on multimodal learning and integration of audio-visual data in ASR systems. The main contribution of this paper is the introduction of the TE2SL framework, which enhances text-only domain adaptation in LLM-based ASR by generating expressive pseudo-audio prompts through a learnable refinement module. This work represents a significant advancement in bridging the modality gap in ASR systems, with promising implications for improving performance in data-scarce environments.
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