Speech Generation for Human-Computer Interaction

人机交互的语音生成

• 批准号: 9528990
• 负责人: Mari Ostendorf
• 金额: $ 23.96万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-05-01 至 1999-09-27
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9528990&HistoricalAwards=false
• 关键词: Speech; Generation; Human; Computer; Interaction

The goal of this project is to increase effectiveness of human-computer communication by improving the quality of automatically generated speech. Using the known linguistic structure that is a by-product of text generation, the research investigates both utterance-level and dialog-level control of prosody (i.e. phrasing, emphasis and intonation) i n a commercial synthesizer. The approach -- development of statistical models of the mapping between meaning and prosodic structure -- is novel in its emphasis on automatic learning algorithms associated with the aim of portability to different task domains/generators. The research involves: collection and automatic prosodic labeling of `acted` speech in target task domains; use of generated syntactic, semantic and discourse annotation to drive prosodic control modules; investigation of the role of prosody in computer initiative, e.g. in clarification or error correction subdialogs; and development of evaluation protocols for assessing speech generation quality and its impact on human-computer interaction. The research will benefit existing spoken language interfaces by providing higher quality speech output and more flexibility for response generation, but it will also open up applications for human-computer interaction where a visual display is not available, e.g. in small devices, in telephone-based computer access or for persons who are visually impaired.

该项目的目标是通过提高自动生成语音的质量来提高人机通信的有效性。利用已知的语言结构，这是一个副产品的文本生成，本研究探讨了话语级和对话级的韵律控制（即措辞，重点和语调）在商业合成器。 的方法-意义和韵律结构之间的映射的统计模型的发展-是新颖的，其重点是自动学习算法与不同的任务域/发电机的可移植性的目的。 研究内容包括：“acted”的韵律提取与自动标注 目标任务域中的语音;使用生成的语法、语义和话语注释来驱动韵律控制模块;调查韵律在计算机主动性中的作用，例如在澄清或纠错子对话框中;以及开发评估协议，用于评估语音生成质量及其对人机交互的影响。 这项研究将通过提供更高质量的语音输出和更灵活的响应生成来使现有的口语界面受益，但它也将为没有视觉显示的人机交互开辟应用程序，例如在小型设备中，基于电话的计算机访问或视障人士。