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In real-world applications, users often require both translations andtranscriptions of speech to enhance their comprehension, particularly instreaming scenarios where incremental generation is necessary. This paperintroduces a streaming Transformer-Transducer that jointly generates automaticspeech recognition (ASR) and speech translation (ST) outputs using a singledecoder. To produce ASR and ST content effectively with minimal latency, wepropose a joint token-level serialized output training method that interleavessource and target words by leveraging an off-the-shelf textual aligner.Experiments in monolingual (it-en) and multilingual (\{de,es,it\}-en) settingsdemonstrate that our approach achieves the best quality-latency balance. Withan average ASR latency of 1s and ST latency of 1.3s, our model shows nodegradation or even improves output quality compared to separate ASR and STmodels, yielding an average improvement of 1.1 WER and 0.4 BLEU in themultilingual case.

Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments