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

RI：媒介：协作研究：口语显式发音模型及其在自动语音识别中的应用

• 批准号: 0905633
• 负责人: Karen Livescu
• 金额: $ 43.88万
• 依托单位: Toyota Technological Institute at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905633&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Explicit

This 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 speaking style, including speaking rate changes and coarticulation. Models of the articulators (such as the lips and tongue) can succinctly represent much of this variability. Most previous work on articulatory models has focused on the relationship between acoustics and articulation, but more significant improvements require models of the hidden articulatory state structure. This work has both a technological goal of improving recognition and a scientific goal of better understanding articulatory phenomena.The project considers larger model classes than previously studied. In particular, the project develops graphical models, including dynamic Bayesian networks and conditional random fields, designed to take advantage of articulatory knowledge. A new framework for hybrid directed and undirected graphical models is being developed, in recognition of the benefits of both directed and undirected models, and of both generative and discriminative training. The project activities include major extension of earlier articulatory models with context modeling, asynchrony structures, and specialized training; development of factored conditional random field models of articulatory variables; and discriminative training to alleviate word confusability.The scientific goal addresses questions about the ways in which articulatory trajectories vary in different contexts. Existing databases are used, and initial work in manual articulatory annotation is being extended. In addition, the project uses articulatory models to perform forced transcription of larger data sets, providing an additional resource for the research community. Other broad impacts include new models and techniques with applicability to other time-series modeling problems. Extending the applicability of speech recognition will help it fulfill its promise of enabling more efficient storage of and access to spoken information, and equalizing the technological playing field for those with hearing or motor disabilities.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。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。 发音器官的模型（如嘴唇和舌头）可以简洁地表示这种变化的大部分。 大多数以前的发音模型的工作都集中在声学和发音之间的关系，但更显着的改进需要隐藏的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。 特别是，该项目开发图形模型，包括动态贝叶斯网络和条件随机场，旨在利用发音知识。 一个新的框架，混合有向和无向图形模型正在开发中，在承认的好处，有向和无向模型，生成和歧视性的培训。 该项目的活动包括：对早期的发音模型进行重大扩展，包括语境建模、语音结构和专门培训;发展发音变量的因子化条件随机场模型;以及减少单词混淆性的辨别性培训。 使用现有的数据库，并在手动发音注释的初步工作正在扩大。 此外，该项目使用发音模型来执行较大数据集的强制转录，为研究社区提供额外的资源。 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实现其承诺，即更有效地存储和访问语音信息，并为听力或运动障碍者提供平等的技术竞争环境。