RI: Medium: Collaborative Research: Explicit Articulatory Models of Spoken Language, with Application to Automatic Speech Recognition

提案标题：国际扶轮：媒介：协作研究：应用于自动语音识别的Spoken语言的显式构思模型机构：芝加哥丰田理工学院摘要日期：05/22/09该奖项由2009年美国复苏和再投资法案(公共法律111-5)资助。自动语音识别的主要挑战之一是说话风格的变化，包括语速变化和协同发音。发音器的模型(如嘴唇和舌头)可以简洁地表示这种可变性。以前对发音模型的研究主要集中在声学和发音之间的关系上，但更重要的改进需要隐含发音状态结构的模型。这项工作既有提高识别能力的技术目标，也有更好地理解发音现象的科学目标。该项目考虑了比以前研究的更大的模型类。特别是，该项目开发了图形模型，包括动态贝叶斯网络和条件随机场，旨在利用发音知识。在认识到定向和非定向模型以及生成性和歧视性训练的益处的情况下，正在开发一种混合定向和非定向图形模型的新框架。项目活动包括对早期发音模型的主要扩展，包括上下文建模、异步结构和专门培训；开发发音变量的分解条件随机场模型；以及减轻单词混淆的区别性培训。科学目标解决了发音轨迹在不同上下文中如何变化的问题。使用了现有的数据库，手写发音注释的初步工作正在扩展。此外，该项目使用关节模型对更大的数据集进行强制转录，为研究界提供了额外的资源。其他广泛的影响包括适用于其他时间序列建模问题的新模型和新技术。扩展语音识别的适用性将帮助它实现其承诺，即实现更高效的语音信息存储和访问，并为听力或运动障碍者提供平等的技术领域。国家科学基础建议摘要建议：0905633 PI姓名：利维斯库，卡伦从eJacket打印：06/10/09第1页，共1页

Proposal Title: RI: Medium: Collaborative Research: Explicit Articulatory Models ofSpoken Language, with Application to Automatic SpeechRecognitionInstitution: Toyota Technological Institute at ChicagoAbstract Date: 05/22/09This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5).One of the main challenges in automatic speech recognition is variability in speakingstyle, including speaking rate changes and coarticulation. Models of the articulators(such as the lips and tongue) can succinctly represent much of this variability. Mostprevious work on articulatory models has focused on the relationship between acousticsand articulation, but more significant improvements require models of the hiddenarticulatory state structure. This work has both a technological goal of improvingrecognition and a scientific goal of better understanding articulatory phenomena.The project considers larger model classes than previously studied. In particular, theproject develops graphical models, including dynamic Bayesian networks andconditional random fields, designed to take advantage of articulatory knowledge. A newframework for hybrid directed and undirected graphical models is being developed, inrecognition of the benefits of both directed and undirected models, and of bothgenerative and discriminative training. The project activities include major extension ofearlier articulatory models with context modeling, asynchrony structures, andspecialized training; development of factored conditional random field models ofarticulatory variables; and discriminative training to alleviate word confusability.The scientific goal addresses questions about the ways in which articulatory trajectoriesvary in different contexts. Existing databases are used, and initial work in manualarticulatory annotation is being extended. In addition, the project uses articulatorymodels to perform forced transcription of larger data sets, providing an additionalresource for the research community. Other broad impacts include new models andtechniques with applicability to other time-series modeling problems. Extending theapplicability of speech recognition will help it fulfill its promise of enabling more efficientstorage of and access to spoken information, and equalizing the technological playingfield for those with hearing or motor disabilities.NATIONAL SCIENCE FOUNDATIONProposal AbstractProposal:0905633 PI Name:Livescu, KarenPrinted from eJacket: 06/10/09 Page 1 of 1