In real-world applications, users often require both translations and transcriptions of speech to enhance their comprehension, particularly in streaming scenarios where incremental generation is necessary. This paper introduces a streaming Transformer-Transducer that jointly generates automatic speech recognition (ASR) and speech translation (ST) outputs using a single decoder. To produce ASR and ST content effectively with minimal latency, we propose a joint token-level serialized output training method that interleaves source and target words by leveraging an off-the-shelf textual aligner. Experiments in monolingual (it-en) and multilingual (\{de,es,it\}-en) settings demonstrate that our approach achieves the best quality-latency balance. With an average ASR latency of 1s and ST latency of 1.3s, our model shows no degradation or even improves output quality compared to separate ASR and ST models, yielding an average improvement of 1.1 WER and 0.4 BLEU in the multilingual case.

该论文介绍了一种使用单个解码器共同生成自动语音识别和语音翻译输出的流式Transformer-Transducer，并提出了一种联合令牌级串行输出训练方法以实现有效的ASR和ST内容生成，其平均ASR延迟为1秒，ST延迟为1.3秒，在多语言情况下优化了输出质量表现。

利用文本对齐进行联合流式自动语音识别和语音翻译的分词级被序列化输出训练