EAGER: Mining a Year of Speech

EAGER：挖掘一年的演讲

• 批准号: 1048900
• 负责人: Mark Liberman
• 金额: $ 9.99万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048900&HistoricalAwards=false
• 关键词: EAGER; Mining; Year; Speech

Technologies for storing and processing vast amounts of text are mature and well-defined. In contrast, technologies for browsing or mining content from large collections of non-textual material, especially audio and video, are less well developed. Large sale data mining on text has helped transform the relevant disciplines; the disciplines dealing with spoken language will reap similar benefits from accessible, searchable, large corpora.This project explores the difficult problem of providing rich, intelligent data mining capabilities for a substantial collection of spoken audio data in American and British English. It applies and extends state-of-the-art techniques to offer sophisticated, rapid and flexible access to a richly annotated corpus of a year of speech (about 9,000 hours, 100 million words, or 2 terabytes), derived from the Linguistic Data Consortium, the British National Corpus, and other existing resources. This is ten times more data than has previously been used by researchers in fields such as phonetics, linguistics, and psychology, and 100 to 1,000 times the amounts that are used in common practice.Speech-to-text alignment and search tools will open a new universe of data to researchers in many fields, from linguistics and phonetics to anthropology, speech communication, oral history, and media studies. Audio-video usage on the internet is large and growing at an extraordinary rate, offering increasingly large amounts of an increasingly large range of material. Reliable automatic annotation, indexing and search of this material will allow researchers to examine the distribution of both form and content across time, space, and social structure.

存储和处理大量文本的技术成熟且定义明确。相比之下，来自大量非文本材料（尤其是音频和视频）的浏览或采矿内容的技术效果不佳。文本上的大型销售数据挖掘有助于改变相关学科；涉及口语的学科将从可访问的，可搜索的大型语料库中获得类似的好处。该项目探讨了为在美国和英国英语中提供丰富的，智能数据挖掘能力的困难问题。 它应用并扩展了最先进的技术，以提供一年的语音（约9,000小时，1亿个单词或2个Terabytes）的丰富注释的语料库，可提供精致，快速，灵活的访问权限，这些语料库源自语言数据联盟，英国国家语料库和其他现有资源。这比以前在语音，语言学和心理学等领域使用的研究人员多了十倍，并且是共同实践中使用的数量的100至1,000倍。Spech-to-toxttext的对齐和搜索工具将向许多领域的研究人员打开新的数据宇宙，从语言学和语音学到人类学，语言学，语言学，语言学，语言，或媒体，或媒体，或媒体和媒体和媒体，或媒体和媒体和媒体和媒体和媒体和媒体。 Internet上的Audio-Video使用情况很大，并且以非凡的速度增长，提供了越来越大的材料范围越来越大。可靠的自动注释，索引和搜索此材料将使研究人员能够在时间，空间和社会结构之间检查形式和内容的分布。