标题： 端到端联合目标和非目标说话人语音识别 显示英文标题

标题： End-to-End Joint Target and Non-Target Speakers ASR

摘要： 本文提出了一种新颖的自动语音识别（ASR）系统，该系统可以从多说话人重叠语音中转录个别说话人的语音，并识别他们是否为目标说话人或非目标说话人。 目标说话人ASR系统是一种有前景的方法，可以通过注册目标说话人的信息仅转录目标说话人的语音。 然而，在对话式ASR应用中，通常需要转录目标说话人和非目标说话人的语音以理解交互信息。 为了在单一ASR模型中自然地考虑目标和非目标说话人，我们的想法是扩展基于自回归建模的多说话人ASR系统，以利用目标说话人的注册语音。 我们提出的ASR通过递归生成文本标记以及表示目标或非目标说话人的标记来执行。 我们的实验证明了我们所提出方法的有效性。 显示英文摘要

摘要： This paper proposes a novel automatic speech recognition (ASR) system that can transcribe individual speaker's speech while identifying whether they are target or non-target speakers from multi-talker overlapped speech. Target-speaker ASR systems are a promising way to only transcribe a target speaker's speech by enrolling the target speaker's information. However, in conversational ASR applications, transcribing both the target speaker's speech and non-target speakers' ones is often required to understand interactive information. To naturally consider both target and non-target speakers in a single ASR model, our idea is to extend autoregressive modeling-based multi-talker ASR systems to utilize the enrollment speech of the target speaker. Our proposed ASR is performed by recursively generating both textual tokens and tokens that represent target or non-target speakers. Our experiments demonstrate the effectiveness of our proposed method.