Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points. Existing SCD solutions either require additional ensemble for the time based decisions and recognized word sequences, or implement a tight integration between ASR and SCD, limiting the potential optimum performance for both tasks. To address these issues, we propose a novel framework for the SCD task, where an additional SCD module is built on top of an existing Transformer Transducer ASR (TT-ASR) network. Two variants of the SCD network are explored in this framework that naturally estimate speaker change probability for each word, while allowing the ASR and SCD to have independent optimization scheme for the best performance. Experiments show that our methods can significantly improve the F1 score on LibriCSS and Microsoft call center data sets without ASR degradation, compared with a joint SCD and ASR baseline.

翻译：说话人变更检测（SCD）是提升自动语音识别（ASR）系统识别结果可读性的重要功能，通过在说话人切换点将词汇序列划分为段落实现。现有SCD解决方案要么需要对时序决策和识别词汇序列进行额外集成，要么将SCD与ASR紧密耦合，导致两项任务均无法达到最优性能。针对上述问题，我们提出了一种新型SCD框架，在现有Transformer换能器ASR（TT-ASR）网络上构建附加的SCD模块。该框架探索了两种SCD网络变体，可自然估计每个词汇的说话人变更概率，同时允许ASR与SCD采用独立优化策略以获取最佳性能。实验表明，与联合SCD与ASR基线相比，本方法在LibriCSS和微软呼叫中心数据集上显著提升F1得分，且未造成ASR性能退化。