发表机构
Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出以语音优先的AI系统Conversation Coach，解决管理者职场对话练习的成本问题，对比端到端与级联语音模型并部署落地，获4万余名管理者使用。
AI 中文摘要
有效的管理者与员工沟通对保留高绩效员工和培养绩效不佳员工至关重要，但培训管理者掌握这些技能的成本仍然很高。基于文本的聊天机器人提供了一种可扩展的方法，但无法提供真实的演练：管理者需要练习大声说话，以便在高风险对话前建立信心。在本文中，我们提出了Conversation Coach，一种以语音优先的AI系统，使管理者能够以真实的口语形式演练高难度职场对话。该系统解决了三个挑战：实现低延迟交互并具备强大的语言理解能力；通过可配置的机器人人格实现自适应对话，以模拟不同类型的员工；以及生成关于内容和政策合规性的个性化反馈。我们将端到端的语音到语音模型与结合自动语音识别、大语言模型和文本到语音合成的级联方法进行比较。端到端方法实现了3倍更低的中位数（P50）延迟，具备原生插话功能，估计成本降低8倍，而级联方法提供了教练质量所需的卓越推理能力。我们在生产环境中部署了级联架构，六个月内有超过4万名管理者使用，采用模式表明其被选择性用于高难度对话。
英文摘要
Effective manager-employee communication is critical for retaining high performers and developing underperformers, yet training managers in these skills remains costly. Text-based chatbots offer a scalable approach but cannot provide realistic rehearsal: managers need to practice speaking aloud to build confidence before high-stakes conversations. In this paper, we propose Conversation Coach, a voice-first AI system that enables managers to rehearse difficult workplace conversations in a realistic spoken format. The system addresses three challenges: achieving low-latency interactions with strong language understanding, enabling adaptive conversations through configurable bot personalities that simulate different employee types, and generating personalized feedback on content and policy compliance. We compare an end-to-end speech-to-speech model with a cascaded approach combining automatic speech recognition, a large language model, and text-to-speech synthesis. The end-to-end approach achieves 3$\times$ lower median (P50) latency with native barge-in capability at an estimated 8$\times$ lower cost, while the cascaded approach offers superior reasoning essential for coaching quality. We deployed the cascaded architecture in production, where 40,000+ managers used it over six months, with adoption patterns indicating selective use for difficult conversations.
Journal refEMNLP 2026 Industry Track
