TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as "talk moves" within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application's cloud-based infrastructure for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss several technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms.

TalkMoves是一款应用程序设计用于支持K-12数学教师反思和持续改进他们的教学实践。该应用程序结合了最先进的自然语言处理技术和自动语音识别技术，自动分析课堂录音，并为教师提供有关他们使用特定类型的讨论来扩宽和深化数学课堂对话的个性化反馈。

使用Transformer为教师提供个性化反馈，帮助改进课堂话语：TalkMoves应用