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    • 3. 发明授权
    • Dialog repair based on discrepancies between user model predictions and speech recognition results
    • 基于用户模型预测和语音识别结果之间的差异的对话框修复
    • US08244545B2
    • 2012-08-14
    • US11393321
    • 2006-03-30
    • Timothy S. PaekDavid M. Chickering
    • Timothy S. PaekDavid M. Chickering
    • G10L21/00
    • G10L15/22G10L2015/228
    • An architecture is presented that leverages discrepancies between user model predictions and speech recognition results by identifying discrepancies between the predictive data and the speech recognition data and repairing the data based in part on the discrepancy. User model predictions predict what goal or action speech application users are likely to pursue based in part on past user behavior. Speech recognition results indicate what goal speech application users are likely to have spoken based in part on words spoken under specific constraints. Discrepancies between the predictive data and the speech recognition data are identified and a dialog repair is engaged for repairing these discrepancies. By engaging in repairs when there is a discrepancy between the predictive results and the speech recognition results, and utilizing feedback obtained via interaction with a user, the architecture can learn about the reliability of both user model predictions and speech recognition results for future processing.
    • 提出了一种通过识别预测数据和语音识别数据之间的差异以及部分地基于差异来修复数据来利用用户模型预测和语音识别结果之间的差异的架构。 用户模型预测部分地基于过去的用户行为来预测用户可能追求的目标或动作语音应用程序。 语音识别结果表明,目标语音应用程序用户可能部分地基于特定约束条件下所说的话语言。 识别预测数据和语音识别数据之间的差异,并进行对话修复以修复这些差异。 通过在预测结果和语音识别结果之间存在差异并利用通过与用户的交互获得的反馈来进行维修,架构可以了解用户模型预测和语音识别结果的可靠性以供将来处理。
    • 8. 发明授权
    • Systems and methods for new time series model probabilistic ARMA
    • 新时间序列模型概率ARMA的系统和方法
    • US07580813B2
    • 2009-08-25
    • US10463145
    • 2003-06-17
    • Bo ThiessonChristopher A. MeekDavid M. ChickeringDavid E. Heckerman
    • Bo ThiessonChristopher A. MeekDavid M. ChickeringDavid E. Heckerman
    • G06F17/50G05B23/02
    • G06F17/18
    • The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.
    • 本发明利用交叉预测方案来预测离散和连续时间观测数据的值,其中每个连续时间管变量的条件方差固定为小的正值。 通过在基于ARMA的模型中允许交叉预测,可以准确预测时间序列中连续和离散观测值。 本发明通过扩展ARMA模型来实现这一目的,使得第一时间序列“管”用于促进或“交叉预测”第二时间序列管中的值以形成“ARMAxp”模型。 一般来说,在ARMAxp模型中,每个连续变量的分布是仅在离散变量上分裂并具有在所有叶上具有连续回归的线性回归的决策图,并且每个离散变量的分布是仅分解为 离散变量,并在所有叶子上具有额外的分布。