arXiv:2211.02499cs.CL
A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
In this paper, we introduce our work of building a Streaming Multilingual Speech Model (SM2), which can transcribe or translate multiple spoken languages into texts of the target language. The backbone of SM2 is Transformer Transducer, which has high streaming capability. Instead of human labeled speech translation (ST) data, SM2 models are trained using weakly supervised data generated by converting the transcriptions in speech recognition corpora with a machine translation service. With 351 thousand hours of anonymized speech training data from 25 languages, SM2 models achieve comparable or even better ST quality than some recent popular large-scale non-streaming speech models. More importantly, we show that SM2 has the truly zero-shot capability when expanding to new target languages, yielding high quality ST results for {source-speech, target-text} pairs that are not seen during training.
Whisper doesn’t do streaming transcription or translation. It’s one of its architectural disadvantages over more commonly used ASR approaches, such as transducers (see e.g. Microsoft Research’s system trained on ~350k hours that does streaming transcription and translation…
woodsonView on HNParent context