Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data
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Computer Science > Computation and Language
Title:Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data
Abstract:Large-scale mined corpora provide abundant training data for end-to-end speech-to-speech translation (S2ST) but may contain noise, misalignment, and semantic errors. Filtering noisy data is crucial to maintain robust speech translation performance. We study how to train an audio-language model to make keep/drop decisions on paired speech directly from audio. To obtain reliable supervision without manual labels, we adopt a scalable two-stage Rank-to-Distill strategy. A lightweight ranker generates keep/drop pseudo-labels from noisy speech pairs, then trains an audio large language model to predict keep/drop directly from raw paired speech. The resulting model jointly captures acoustic fidelity and cross-lingual semantic consistency for the selection of speech-conditioned data. Experiments on CVSS-C and SpeechMatrix show consistent improvements over unfiltered training, yielding up to +1.4 ASR-BLEU for end-to-end S2ST.
| Comments: | Accepted to INTERSPEECH 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.13507 [cs.CL] |
| (or arXiv:2606.13507v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.13507
arXiv-issued DOI via DataCite (pending registration)
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