Language Generation for a Speech Prosthesis

语音假体的语言生成

• 批准号: 9612682
• 负责人: Ivan Sag
• 金额: $ 78.1万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-15 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9612682&HistoricalAwards=false
• 关键词: Language; Generation; Speech; Prosthesis

This project is developing a novel approach to natural language generation, applying it to computer-aided text and speech generation for people with physical disabilities. Many people who cannot speak because of physical disability utilize text-to- speech generators as prosthetic devices. However, users of speech prostheses often have more general loss of motor control, and despite aids such as word prediction, text entry is slow and difficult. For typical users, current speech prostheses have output rates less than a tenth of the speed of normal speech. This prevents natural social conversation, since it completely disrupts the usual processes of turn-taking, and can lead to negative effects on the listener's attitude to the prosthesis user. The main focus of this research is the investigation of techniques which can improve rates sufficiently for more natural conversation to be possible, without sacrificing flexibility of content. This new approach employs a combination of a wide- coverage grammar, corpus-based word frequency data, and conversational templates. Applied to speech prosthesis, it enables the production of full sentences from minimal user input in a context-sensitive way. The approach can also be applied more generally for efficient production of formulaic text like the structured reports used widely in business and government.

该项目正在开发一种新的自然语言生成方法，将其应用于计算机辅助文本， 为身体残疾的人提供语音生成。 许多因身体残疾而不能说话的人使用文本到语音生成器作为假肢设备。 然而，语音假体的使用者通常会有更普遍的运动控制丧失，尽管有单词预测等辅助手段，文本输入仍然缓慢而困难。 对于典型的使用者来说，目前的语音假体的输出速率小于正常语音速度的十分之一。 这阻止了自然的社交对话，因为它完全破坏了通常的话轮转换过程，并可能导致对听者对假肢使用者的态度产生负面影响。 这项研究的主要重点是调查的技术，可以充分提高利率更自然的对话是可能的，而不牺牲内容的灵活性。 这种新方法采用了广泛的覆盖范围， 语法， 语料库词频 数据， 和会话模板。 应用于语音假体，它使生产完整的句子，从最小的用户输入在上下文敏感的方式。 这种方法也可以更普遍地应用于公式化文本的高效制作，如广泛用于商业和政府的结构化报告。