SSEmb: Training a Generalizable Speech Retrieval Model Using Only Automatic Speech Recognition Data

Abstract

End-to-end speech-to-text retrieval is becoming an important capability for voice agents, allowing spoken queries to retrieve relevant documents without intermediate transcription. Training these retrievers typically depends on large collections of spoken query-document pairs that are costly to acquire and difficult to extend across domains and languages. We introduce \ours, which replaces such task-specific supervision with speech-transcript pairs from standard ASR corpora. A lightweight adapter projects the output of a frozen speech encoder into the retrieval space of a frozen pretrained text embedding model. \ours is competitive with cascaded ASR-then-retrieve systems while reducing inference latency, and outperforms existing publicly available speech embedding models. It also transfers to text-to-speech and speech-to-speech retrieval without additional training. Controlled analyses validate the speech-transcript alignment paradigm, demonstrating robustness to reduced training data and consistent gains from larger corpora. Further ablations of loss functions and trainable modules establish practical guidelines for the framework. Model weights and codes are to be released.