Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts

Abstract: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.

Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)

arXiv:2609.21844 [cs.CL]

(or arXiv:2609.21844v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.21844 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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