基于 Transducer 的多说话人语音翻译中的流式说话人变更检测与性别分类
Streaming Speaker Change Detection and Gender Classification for Transducer-Based Multi-Talker Speech Translation
AI总结：
本文提出将说话人嵌入整合到基于 Transducer 的流式端到端语音翻译模型中，以同时实现多说话人场景下的流式说话人变更检测与性别分类，实验验证了该方法的高准确率。
摘要：
流式多说话人语音翻译任务不仅需要以低延迟生成准确、流畅的翻译，还需要识别说话人何时发生变更以及说话人的性别。说话人变更信息可用于为零样本文本到语音系统创建音频提示，而性别信息有助于在传统文本到语音模型中选择说话人配置文件。我们提出通过将说话人嵌入整合到基于 Transducer 的流式端到端语音翻译模型中，来解决流式说话人变更检测与性别分类问题。我们的实验表明，所提出的方法在说话人变更检测和性别分类上均能取得较高的准确率。
英文摘要：
Streaming multi-talker speech translation is a task that involves not only generating accurate and fluent translations with low latency but also recognizing when a speaker change occurs and what the speaker's gender is. Speaker change information can be used to create audio prompts for a zero-shot text-to-speech system, and gender can help to select speaker profiles in a conventional text-to-speech model. We propose to tackle streaming speaker change detection and gender classification by incorporating speaker embeddings into a transducer-based streaming end-to-end speech translation model. Our experiments demonstrate that the proposed methods can achieve high accuracy for both speaker change detection and gender classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
