Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data

• URL: http://arxiv.org/abs/2606.13507v1
• Date: Thu, 11 Jun 2026 15:55:23 GMT
• Title: Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data
• Abstract summary: We study how to train an audio-language model to make keep/drop decisions on paired speech directly from audio.<n>A lightweight ranker generates keep/drop pseudo-labels from noisy speech pairs, then trains an audio large language model to predict keep/drop.<n>Experiments on CVSS-C and SpeechMatrix show consistent improvements over unfiltered training, yielding up to +1.4 ASR-BLEU for end-to-end S2ST.
• Score: 5.0319300412852765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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