Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts
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Abstract (arXiv)
Long transcripts are costly inputs for downstream NLP systems and often contain irrelevant context. We study query-conditioned topic localization: predicting the sentence span in a transcript that best addresses a topic-title query. To improve span localization, we reuse ASR encoder states as sentence-level representations and fuse them with textual embeddings. This lets lightweight span locators exploit speech information without running a separate audio encoder. Experiments on two public datasets show consistent gains over text-only baselines, especially under strict boundary-matching criteria. Cross-dataset experiments further indicate that the benefits are strongest for structured or semi-structured speech, while gains on spontaneous speech are limited and mixed.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 47 references that are in the map. First posted on arXiv 18 Sep 2026.
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References · 30
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18 more
- 2020Span-based Localizing Network for Natural Language Video Localizationcited by 234
- 2025NoLiMa: Long-Context Evaluation Beyond Literal Matchingcited by 120
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- 2023Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggerscited by 72
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- 2026Reading Between the Waves: Robust Topic Segmentation Using Inter-Sentence Audio Featurescited by 1
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