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In this paper, we introduce our work of building a Streaming MultilingualSpeech Model (SM2), which can transcribe or translate multiple spoken languagesinto texts of the target language. The backbone of SM2 is TransformerTransducer, which has high streaming capability. Instead of human labeledspeech translation (ST) data, SM2 models are trained using weakly superviseddata generated by converting the transcriptions in speech recognition corporawith a machine translation service. With 351 thousand hours of anonymizedspeech training data from 25 languages, SM2 models achieve comparable or evenbetter ST quality than some recent popular large-scale non-streaming speechmodels. More importantly, we show that SM2 has the truly zero-shot capabilitywhen expanding to new target languages, yielding high quality ST results for{source-speech, target-text} pairs that are not seen during training.

A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability