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1. 发明申请

US20070239453A1 AUGMENTING CONTEXT-FREE GRAMMARS WITH BACK-OFF GRAMMARS FOR PROCESSING OUT-OF-GRAMMAR UTTERANCES 审中-公开
标题翻译：使用后处理灰度来处理超出灰度特征的无限自由的GRAMMARS
公开(公告)号：US20070239453A1
公开(公告)日：2007-10-11
申请号：US11278893
申请日：2006-04-06
申请人： Timothy Paek , David Chickering , Eric Badger , Qiang Wu
发明人： Timothy Paek , David Chickering , Eric Badger , Qiang Wu
IPC分类号： G10L15/18
CPC分类号： G10L15/065
摘要： Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.
摘要翻译：用于在语音识别应用程序中集成和生成后退语法（BOG）的体系结构，用于识别语法（OOG）语音并更新无上下文语法（CFG）。解析组件识别来自用户话语的关键字和/或时隙，并且语法生成组件在关键词和时隙之前和之后添加填充标签以创建新的语法规则。 BOG可以从这些新的语法规则生成，并可用于处理OOG用户的话语。通过通过BOG处理OOG用户话语，架构可以代表用户识别并执行预期的任务。

2. 发明申请

US20070239454A1 PERSONALIZING A CONTEXT-FREE GRAMMAR USING A DICTATION LANGUAGE MODEL 有权
标题翻译：使用引用语言模型个性化无背景GRAMMAR
公开(公告)号：US20070239454A1
公开(公告)日：2007-10-11
申请号：US11278899
申请日：2006-04-06
申请人： Timothy Paek , David Chickering , Eric Badger , Qiang Wu
发明人： Timothy Paek , David Chickering , Eric Badger , Qiang Wu
IPC分类号： G10L15/18
CPC分类号： G10L15/19 , G10L2015/088
摘要： Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.
摘要翻译：用于在语音识别应用程序中集成和生成后退语法（BOG）的体系结构，用于识别语法（OOG）语音并更新无上下文语法（CFG）。解析组件识别来自用户话语的关键字和/或时隙，并且语法生成组件在关键词和时隙之前和之后添加填充标签以创建新的语法规则。 BOG可以从这些新的语法规则生成，并可用于处理OOG用户的话语。通过通过BOG处理OOG用户话语，架构可以代表用户识别并执行预期的任务。

3. 发明授权

US08108205B2 Leveraging back-off grammars for authoring context-free grammars 有权
标题翻译：利用后备语法创作无上下文的语法
公开(公告)号：US08108205B2
公开(公告)日：2012-01-31
申请号：US11607284
申请日：2006-12-01
申请人： Timothy Paek , Max Chickering , Eric Badger
发明人： Timothy Paek , Max Chickering , Eric Badger
IPC分类号： G06F17/27
CPC分类号： G06F17/271
摘要： A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.
摘要翻译：一种改进上下文无关语法（CFG）的系统和方法。该方法包括从最初开发的CFG导出回退语法（BOG）规则，并利用初始CFG和派生的BOG规则来识别用户话语。基于初始CFG和派生BOG规则对用户话语的响应，使用派生BOG规则的至少一部分来修改初始CFG，从而产生精炼的CFG。上述方法可以进行迭代，每个新迭代利用前面迭代的精细CFG。

4. 发明授权

US08862468B2 Leveraging back-off grammars for authoring context-free grammars 有权
标题翻译：利用后备语法创作无上下文的语法
公开(公告)号：US08862468B2
公开(公告)日：2014-10-14
申请号：US13334580
申请日：2011-12-22
申请人： Timothy Paek , Max Chickering , Eric Badger
发明人： Timothy Paek , Max Chickering , Eric Badger
IPC分类号： G10L15/06 , G06F17/20 , G06F17/28 , G06F17/27
CPC分类号： G06F17/271
摘要： A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.
摘要翻译：一种改进上下文无关语法（CFG）的系统和方法。该方法包括从最初开发的CFG导出回退语法（BOG）规则，并利用初始CFG和派生的BOG规则来识别用户话语。基于初始CFG和派生BOG规则对用户话语的响应，使用派生BOG规则的至少一部分来修改初始CFG，从而产生精炼的CFG。上述方法可以进行迭代，每个新迭代利用前面迭代的精细CFG。

5. 发明申请

US20120095752A1 LEVERAGING BACK-OFF GRAMMARS FOR AUTHORING CONTEXT-FREE GRAMMARS 有权
标题翻译：用于执行无背景GRAMMARS的后备格式
公开(公告)号：US20120095752A1
公开(公告)日：2012-04-19
申请号：US13334580
申请日：2011-12-22
申请人： Timothy Paek , Max Chickering , Eric Badger
发明人： Timothy Paek , Max Chickering , Eric Badger
IPC分类号： G10L15/18 , G06F17/27
CPC分类号： G06F17/271
摘要： A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.
摘要翻译：一种改进上下文无关语法（CFG）的系统和方法。该方法包括从最初开发的CFG导出回退语法（BOG）规则，并利用初始CFG和派生的BOG规则来识别用户话语。基于初始CFG和派生BOG规则对用户话语的响应，使用派生BOG规则的至少一部分来修改初始CFG，从而产生精炼的CFG。上述方法可以进行迭代，每个新迭代利用前面迭代的精细CFG。

6. 发明申请

US20080133220A1 Leveraging back-off grammars for authoring context-free grammars 有权
标题翻译：利用后备语法创作无上下文的语法
公开(公告)号：US20080133220A1
公开(公告)日：2008-06-05
申请号：US11607284
申请日：2006-12-01
申请人： Timothy Paek , Max Chickering , Eric Badger
发明人： Timothy Paek , Max Chickering , Eric Badger
IPC分类号： G06F17/27
CPC分类号： G06F17/271
摘要： A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.
摘要翻译：一种改进上下文无关语法（CFG）的系统和方法。该方法包括从最初开发的CFG导出回退语法（BOG）规则，并利用初始CFG和派生的BOG规则来识别用户话语。基于初始CFG和派生BOG规则对用户话语的响应，使用派生BOG规则的至少一部分来修改初始CFG，从而产生精炼的CFG。上述方法可以进行迭代，每个新迭代利用前面迭代的精细CFG。

7. 发明申请

US20060224535A1 Action selection for reinforcement learning using influence diagrams 失效
标题翻译：使用影响图强化学习的行动选择
公开(公告)号：US20060224535A1
公开(公告)日：2006-10-05
申请号：US11169503
申请日：2005-06-29
申请人： David Chickering , Timothy Paek , Eric Horvitz
发明人： David Chickering , Timothy Paek , Eric Horvitz
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： A system and method for online reinforcement learning is provided. In particular, a method for performing the explore-vs.-exploit tradeoff is provided. Although the method is heuristic, it can be applied in a principled manner while simultaneously learning the parameters and/or structure of the model (e.g., Bayesian network model). The system includes a model which receives an input (e.g., from a user) and provides a probability distribution associated with uncertainty regarding parameters of the model to a decision engine. The decision engine can determine whether to exploit the information known to it or to explore to obtain additional information based, at least in part, upon the explore-vs.-exploit tradeoff (e.g., Thompson strategy). A reinforcement learning component can obtain additional information (e.g., feedback from a user) and update parameter(s) and/or the structure of the model. The system can be employed in scenarios in which an influence diagram is used to make repeated decisions and maximization of long-term expected utility is desired.
摘要翻译：提供了一种在线强化学习的系统和方法。特别地，提供了用于执行探索与利用的权衡的方法。尽管该方法是启发式的，但是它可以以原则的方式应用，同时学习模型的参数和/或结构（例如，贝叶斯网络模型）。该系统包括接收输入（例如，来自用户）并且向决策引擎提供与关于模型的参数的不确定性相关联的概率分布的模型。决策引擎可以确定是否利用已知的信息，或者至少部分地基于探索与利用权衡（Thompson策略）来探索获取附加信息。强化学习组件可以获得附加信息（例如，来自用户的反馈）和更新参数和/或模型的结构。该系统可用于使用影响图进行重复决策的场景，并期望实现长期预期效用的最大化。

8. 发明申请

US20060206337A1 Online learning for dialog systems 有权
标题翻译：在线学习对话系统
公开(公告)号：US20060206337A1
公开(公告)日：2006-09-14
申请号：US11170999
申请日：2005-06-29
申请人： Timothy Paek , David Chickering , Eric Horvitz
发明人： Timothy Paek , David Chickering , Eric Horvitz
IPC分类号： G10L21/00
CPC分类号： G10L15/065
摘要： An online dialog system and method are provided. The dialog system receives speech input and outputs an action according to its models. After executing the action, the system receives feedback from the environment or user. The system immediately utilizes the feedback to update its models in an online fashion.
摘要翻译：提供在线对话系统和方法。对话系统接收语音输入，并根据其模型输出动作。执行该操作后，系统会收到来自环境或用户的反馈。系统立即利用反馈以在线方式更新其模型。

9. 发明申请

US20060206333A1 Speaker-dependent dialog adaptation 审中-公开
标题翻译：与扬声器相关的对话框适应
公开(公告)号：US20060206333A1
公开(公告)日：2006-09-14
申请号：US11170998
申请日：2005-06-29
申请人： Timothy Paek , David Chickering , Eric Horvitz
发明人： Timothy Paek , David Chickering , Eric Horvitz
IPC分类号： G10L13/00
CPC分类号： G10L15/22 , G10L15/07
摘要： A simulation environment for adapting a speech model (e.g., baseline model) to a user is provided. The user can interact with a base parametric speech model (e.g., statistical model with learnable parameters such as a Bayesian network) and give positive and/or negative feedback when the dialog system has performed what the user considers to be appropriate and/or inappropriate action(s). From the user feedback, the dialog system learns to take actions customized for the particular user. Speaker-dependent adaptation can be extended to the dialog level by performing maximum likelihood linear regression (MLLR) adaptation simultaneously with dialog personalization. Users are immediately able to observe how their feedback has caused the dialog system to adapt, and can quit training whenever they feel that the dialog system has adapted enough for current purposes.
摘要翻译：提供了一种用于将语音模型（例如，基准模型）适配到用户的模拟环境。用户可以与基本参数语音模型（例如，具有诸如贝叶斯网络的可学习参数的统计模型）交互，并且当对话系统执行用户认为是适当的和/或不适当的动作时给出正和负反馈（s）。从用户反馈中，对话系统学习采取针对特定用户定制的动作。通过与对话个性化同时执行最大似然线性回归（MLLR）适应，可以将扬声器依赖的适应扩展到对话级。用户可以立即观察他们的反馈如何使对话系统适应，并且只要他们觉得对话系统已经足够适应当前的目的，就可以退出训练。

10. 发明申请

US20070239637A1 Using predictive user models for language modeling on a personal device 失效
标题翻译：在个人设备上使用预测用户模型进行语言建模
公开(公告)号：US20070239637A1
公开(公告)日：2007-10-11
申请号：US11378024
申请日：2006-03-17
申请人： Timothy Paek , David Chickering
发明人： Timothy Paek , David Chickering
IPC分类号： G06F15/18
CPC分类号： G06F17/276 , G06N99/005 , G10L15/183 , G10L15/22 , G10L2015/0631
摘要： A system and method for prediction of a user goal for command/control of a personal device (e.g., mobile phone) is provided. The system employs statistical model(s) that can predict a command based, at least in part, on past user behavior (e.g., probability distribution over a set of predicates, and, optionally arguments). Further, the system can be employed with a speech recognition component to facilitate language modeling for predicting the user goal. The system can include predictive user models (e.g., predicate model and argument model) that receive a user input (e.g., utterance) and employ statistical modeling to determine the likely command without regard to the actual content of the input (e.g., utterance). The system employs features for predicting the next user goal which can be stored in a user data store. Features can capture personal idiosyncrasies or systematic patterns of usage (e.g., device-related, time-related, predicate-related, contact-specific and/or periodic features).
摘要翻译：提供了一种用于预测用于个人设备（例如，移动电话）的命令/控制的用户目标的系统和方法。该系统使用至少部分地基于过去的用户行为（例如，一组谓词上的概率分布，以及可选的参数）来预测命令的统计模型。此外，该系统可以与语音识别组件一起使用以便于用于预测用户目标的语言建模。该系统可以包括接收用户输入（例如，话语）并且采用统计建模来确定可能的命令而不考虑输入的实际内容（例如，话语）的预测用户模型（例如谓词模型和参数模型）。该系统采用用于预测可存储在用户数据存储中的下一个用户目标的特征。特征可以捕获个人特征或系统的使用模式（例如，与设备相关的，与时间相关的，谓词相关的，特定于接触的和/或周期的特征）。

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