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In this study, we present synchronous bilingual Connectionist TemporalClassification (CTC), an innovative framework that leverages dual CTC to bridgethe gaps of both modality and language in the speech translation (ST) task.Utilizing transcript and translation as concurrent objectives for CTC, ourmodel bridges the gap between audio and text as well as between source andtarget languages. Building upon the recent advances in CTC application, wedevelop an enhanced variant, BiL-CTC+, that establishes new state-of-the-artperformances on the MuST-C ST benchmarks under resource-constrained scenarios.Intriguingly, our method also yields significant improvements in speechrecognition performance, revealing the effect of cross-lingual learning ontranscription and demonstrating its broad applicability. The source code isavailable at https://github.com/xuchennlp/S2T.

Bridging the Gaps of Both Modality and Language: Synchronous Bilingual CTC for Speech Translation and Speech Recognition