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

提案标题：RI：媒介：合作研究：口语明确发音模型及其在自动语音识别中的应用机构：芝加哥丰田技术研究所摘要：日期：05/22/2009该奖项由2009年美国复苏和再投资法案（公法111-5）资助。自动语音识别的主要挑战之一是说话风格的可变性，包括语速变化和协同发音。发音器官（如嘴唇和舌头）的模型可以简洁地表示这种变化。先前关于发音模型的大部分工作都集中在声学和发音之间的关系上，但更重要的改进需要隐藏发音状态结构的模型。这项工作既有提高识别的技术目标，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过的更大的模型类。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。有向和无向混合图形模型的新框架正在开发中，它没有认识到有向和无向模型的好处，也没有认识到生成和判别训练的好处。项目活动包括对先前的铰接模型的主要扩展，包括上下文建模、异步结构和专门培训；铰接变量因子条件随机场模型的建立以及辨别性训练，以减轻词语混淆。科学目标解决的问题是，发音轨迹在不同的环境中是如何变化的。使用现有的数据库，并且正在扩展手动发音注释的初步工作。此外，该项目使用铰接模型来执行大型数据集的强制转录，为研究界提供额外的资源。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技术。扩展语音识别的适用性将有助于它实现更有效地存储和访问语音信息的承诺，并为听力或运动障碍者提供平等的技术竞争环境。国家科学基金会提案摘要提案：0905633 PI姓名：Livescu， karenejacket: 06/10/09第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