RI: Large: An Integrated Approach to Creating Context Enriched Speech Translation Systems

RI：大型：创建上下文丰富的语音翻译系统的集成方法

• 批准号: 0911009
• 负责人: Shrikanth Narayanan
• 金额: $ 220万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0911009&HistoricalAwards=false
• 关键词: RI; Large; Integrated; Approach; Creating

The project creates robust, widely-deployable and cost-effective technologies for supporting cross-lingual spoken interaction between people who do not share a common language. The target application supports communication between healthcare personnel who speak English only and patients with limited-English proficiency. The state-of-the-art technologies that enable such cross-lingual interactions are characterized by a pipelined architecture of speech recognition, machine translation and speech synthesis, that largely ignore the rich information present in spoken language beyond those conveyed by words. They also do not take advantage of the humans in the loop for collaboratively managing the interaction. Overcoming these limitations requires improving robust intelligence at all levels ? signal, system, and human ? and set the research goals for this project. The project?s intellectual merit comes from the unique combination of theoretical, computational model-ing and empirical elements: The theoretical framework is centered on notions of social co-presence to de-velop new models for translation-mediated communication. The computational modeling focuses on capturing prosody, dialog and user state from spoken language for enriching the technology components. The empirical work relies on a participatory approach to iterative design and evaluation of the system, working directly with the stakeholders.The broader impact can be seen in the potential for facilitating multilingual efforts ranging from disaster relief and global business operations to servicing diverse immigrant populations notably in health care. The effort brings together engineers, linguists, human communication experts, and medical professionals to tackle a broad range of problems, and offers integrated interdisciplinary research training and mentor-ing.

该项目创建了健壮的、可广泛部署的、具有成本效益的技术，以支持不使用共同语言的人之间的跨语言口头交互。目标应用程序支持只会说英语的医护人员与英语水平有限的患者之间的通信。实现这种跨语言交互的最先进技术的特点是语音识别、机器翻译和语音合成的流水线架构，这在很大程度上忽略了口头语言中存在的丰富信息，而不仅仅是文字传达的信息。它们也没有利用循环中的人来协作管理交互。克服这些限制需要在各个层面上提高强大的智力。信号、系统和人？并为这个项目设定研究目标。这个项目吗?本书的学术价值来自于理论、计算模型和实证元素的独特结合：理论框架以社会共临的概念为中心，为翻译介导的交际开发了新的模型。计算建模侧重于从口语中捕获韵律、对话和用户状态，以丰富技术组件。经验性工作依赖于系统迭代设计和评估的参与性方法，直接与利益相关者合作。更广泛的影响可以从促进从救灾和全球商业运作到服务不同移民人口，特别是在保健方面的多语言努力的潜力中看到。该项目汇集了工程师、语言学家、人类交流专家和医学专业人士，共同解决各种各样的问题，并提供综合的跨学科研究培训和指导。