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arxiv: 2605.14340 · v1 · submitted 2026-05-14 · 💻 cs.SD

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Refining Pseudo-Audio Prompts with Speech-Text Alignment for Text-Only Domain Adaptation in LLM-Based ASR

Ryo Magoshi , Takashi Maekaku , Yusuke Shinohara
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Pith reviewed 2026-05-15 02:18 UTC · model grok-4.3

classification 💻 cs.SD
keywords adaptationpromptspseudo-audiotext-onlyalignmentaudiodemonstratedomain
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A speech-text alignment method generates expressive pseudo-audio prompts for effective text-only domain adaptation in LLM-based ASR, outperforming prior text-only approaches on error rates and OOV coverage.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

LLM-based speech recognition systems connect audio encoders to large language models for transcription. Adapting them to new domains is hard because paired speech and text data is scarce. Previous solutions either fine-tune only the language model part, losing acoustic details, or create fake audio prompts from text alone, which often lack expressiveness. This work adds an explicit alignment step between speech and text features when creating those fake prompts. The alignment helps the prompts carry more realistic acoustic information even though no real audio is available for the target domain. Experiments show lower overall error rates and better handling of words not seen in training compared to earlier text-only adaptation techniques.

Core claim

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.

Load-bearing premise

That explicitly modeling speech-text alignment during pseudo-audio prompt generation will produce prompts expressive enough to close the modality gap and yield measurable gains in target-domain ASR without any real audio from that domain.

Figures

Figures reproduced from arXiv: 2605.14340 by Ryo Magoshi, Takashi Maekaku, Yusuke Shinohara.

Figure 1
Figure 1. Figure 1: Overview of the LLM-based ASR framework. The au￾dio encoder and projector generate audio prompts that are con￾catenated with token embeddings and processed by the LLM [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of TE2SL. (a) Training phase: the refinement module learns alignment from LLM token embeddings to audio prompts with pre-trained audio encoder/projector characteristics. (b) Domain adaptation phase: the refinement module is freezed. Text embeddings are randomly upsampled, transformed via the refinement module, and time-masked to generate pseudo-audio prompts [PITH_FULL_IMAGE:figures/full_fig_p004… view at source ↗
read the original 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.

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Works this paper leans on

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