RI: Small: Incremental Speech Processing for Rapid Dialogue

RI：小型：用于快速对话的增量语音处理

• 批准号: 1219253
• 负责人: David DeVault
• 金额: $ 45万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219253&HistoricalAwards=false
• 关键词: RI; Small; Incremental; Speech; Processing

This project develops incremental language processing techniques to enable spoken dialogue systems to communicate in a way that is more highly interactive, more efficient, and more human-like. Most previous dialogue systems have employed a strict turn-taking regime in which one person speaks at a time, no attempt is made to understand or respond to speech until the speaker finishes speaking, and the overall latency in system responses is high in comparison to human-human conversation. This results in systems that are unable to provide a range of rapid and overlapping responses that human interlocutors frequently use to achieve an efficient and successful communication process, including back-channels, interruptions, collaborative completions, clarifications, and other rapid responses. This project is a computational and empirical investigation into how a system's assessment of its own incremental understanding of ongoing user speech can guide its strategic decisions to initiate such rapid and overlapping responses. The feature representations and response policies that can implement this decision-making are studied in the context of two fast-paced interactive dialogue games. These games are carefully chosen to support objective evaluation of incremental response strategies and fun gameplay that facilitates large-scale data collection.The resulting computational models may improve the conversational skills of a range of dialogue systems, including not only game-oriented systems but also practical applications such as intelligent tutoring and training systems, information access systems, and entertainment applications. A second product of this project is an annotated corpus of human-human and human-system dialogue data for use by other researchers. A third product is the incorporation of relevant software into a publicly distributed toolkit for building dialogue systems, supporting further research and education.

该项目开发了增量语言处理技术，使口语对话系统能够以更高交互性，更高效，更人性化的方式进行交流。 大多数以前的对话系统采用严格的话轮转换机制，其中一个人一次说话，在说话者说完之前不尝试理解或响应语音，并且与人与人的对话相比，系统响应的总体延迟很高。 这导致系统无法提供人类对话者经常使用的一系列快速和重叠的响应，以实现有效和成功的通信过程，包括反向通道，中断，协作完成，澄清和其他快速响应。 这个项目是一个计算和实证调查如何系统的评估自己的增量理解正在进行的用户语音可以指导其战略决策，启动这样的快速和重叠的反应。 在两个快节奏的交互式对话游戏的背景下，可以实现这一决策的功能表示和响应策略进行了研究。 这些游戏经过精心挑选，以支持增量响应策略的客观评估和有趣的游戏玩法，从而促进大规模的数据收集。由此产生的计算模型可以提高一系列对话系统的会话技能，不仅包括面向游戏的系统，而且还包括实际应用，如智能辅导和培训系统，信息访问系统和娱乐应用。 该项目的第二个产品是一个注释的人与人和人与系统对话数据的语料库，供其他研究人员使用。 第三个产品是将相关软件纳入一个公开分发的工具包，用于建立对话系统，支持进一步的研究和教育。