Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data
For researchers in speech-to-speech translation, this work provides a scalable data filtering approach that improves translation quality without manual labels.
The authors propose a method to filter noisy speech-to-speech translation training data using an audio-language model, achieving up to +1.4 ASR-BLEU improvement on end-to-end S2ST.
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.