TICL: Text-Embedding KNN For Speech In-Context Learning Unlocks Speech Recognition Abilities of Large Multimodal Models

• URL: http://arxiv.org/abs/2509.13395v1
• Date: Tue, 16 Sep 2025 17:07:23 GMT
• Title: TICL: Text-Embedding KNN For Speech In-Context Learning Unlocks Speech Recognition Abilities of Large Multimodal Models
• Authors: Haolong Zheng, Yekaterina Yegorova, Mark Hasegawa-Johnson,
• Abstract summary: We propose Text-Embedding KNN for SICL (TICL) to enhance off-the-shelf large multimodal models' speech recognition ability without fine-tuning.<n>Our method enables models to surpass zero-shot performance with up to 84.7% relative WER reduction.
• Score: 27.013776992438086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech foundation models have recently demonstrated the ability to perform Speech In-Context Learning (SICL). Selecting effective in-context examples is crucial for SICL performance, yet selection methodologies remain underexplored. In this work, we propose Text-Embedding KNN for SICL (TICL), a simple pipeline that uses semantic context to enhance off-the-shelf large multimodal models' speech recognition ability without fine-tuning. Across challenging automatic speech recognition tasks, including accented English, multilingual speech, and children's speech, our method enables models to surpass zero-shot performance with up to 84.7% relative WER reduction. We conduct ablation studies to show the robustness and efficiency of our method.

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