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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Conversation Coach: A Voice-enabled AI System that Helps Practice Difficult Workplace Conversations
%A Wu, Fanyou
%A Maharjan, Suraj
%A Yessenalina, Ainur
%A Chen, Dennis Xu
%A Srivastava, Rahul
%A Sengamedu, Srinivasan H.
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F wu-etal-2026-conversation
%X 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.
%U https://aclanthology.org/2026.emnlp-industry.77/
%P 1302-1314

Markdown (Informal)

[Conversation Coach: A Voice-enabled AI System that Helps Practice Difficult Workplace Conversations](https://aclanthology.org/2026.emnlp-industry.77/) (Wu et al., EMNLP 2026)

• Conversation Coach: A Voice-enabled AI System that Helps Practice Difficult Workplace Conversations (Wu et al., EMNLP 2026)

ACL

• Fanyou Wu, Suraj Maharjan, Ainur Yessenalina, Dennis Xu Chen, Rahul Srivastava, and Srinivasan H. Sengamedu. 2026. Conversation Coach: A Voice-enabled AI System that Helps Practice Difficult Workplace Conversations. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1302–1314, Budapest, Hungary. Association for Computational Linguistics.