公开(公告)号：US20080205761A1

公开(公告)日：2008-08-28

申请号：US11680566

申请日：2007-02-28

申请人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

发明人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/6807 ,  G06K9/468 ,  G06K2209/01 ,  G06K2209/011

摘要： Exemplary techniques are described for selecting radical sets for use in probabilistic East Asian character recognition algorithms. An exemplary technique includes applying a decomposition rule to each East Asian character of the set to generate a progressive splitting graph where the progressive splitting graph comprises radicals as nodes, formulating an optimization problem to find an optimal set of radicals to represent the set of East Asian characters using maximum likelihood and minimum description length and solving the optimization problem for the optimal set of radicals. Another exemplary technique includes selecting an optimal set of radicals by using a general function that characterizes a radical with respect to other East Asian characters and a complex function that characterizes complexity of a radical.

摘要翻译： 描述了用于选择在概率东亚字符识别算法中使用的激进集合的示例性技术。 一个示例性的技术包括将分解规则应用于集合的每个东亚字符以生成逐行分割图，其中渐进分割图包括基数作为节点，制定优化问题以找到最佳的一组基团以表示东亚集 字符使用最大似然和最小描述长度，并解决优化问题的最佳组的自由基。 另一个示例性技术包括通过使用表征相对于其他东亚字符的基数的一般函数和表征激进的复杂度的复杂函数来选择最佳的自由基集合。

公开(公告)号：US20080219556A1

公开(公告)日：2008-09-11

申请号：US11682722

申请日：2007-03-06

申请人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

发明人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/00879

摘要： Exemplary methods, systems, and computer-readable media for developing, training and/or using models for online handwriting recognition of characters are described. An exemplary method for building a trainable radical-based HMM for use in character recognition includes defining radical nodes, where a radical node represents a structural element of an character, and defining connection nodes, where a connection node represents a spatial relationship between two or more radicals. Such a method may include determining a number of paths in the radical-based HMM using subsequence direction histogram vector (SDHV) clustering and determining a number of states in the radical-based HMM using curvature scale space-based (CSS) corner detection.

摘要翻译： 描述用于开发，训练和/或使用用于字符的在线手写识别的模型的示例性方法，系统和计算机可读介质。 用于构建用于字符识别的可训练基于激进的基于HMM的示例性方法包括定义基本节点，其中基本节点表示字符的结构元素，并且定义连接节点，其中连接节点表示两个或更多个之间的空间关系 激进分子 这种方法可以包括使用子序列方向直方图向量（SDHV）聚类确定基于激进的HMM中的路径数量，并使用基于曲率空间的（CSS）角检测确定基于激进的HMM中的状态数。

公开(公告)号：US07805004B2

公开(公告)日：2010-09-28

申请号：US11680566

申请日：2007-02-28

申请人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

发明人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/62

CPC分类号： G06K9/6807 ,  G06K9/468 ,  G06K2209/01 ,  G06K2209/011

摘要： Exemplary techniques are described for selecting radical sets for use in probabilistic East Asian character recognition algorithms. An exemplary technique includes applying a decomposition rule to each East Asian character of the set to generate a progressive splitting graph where the progressive splitting graph comprises radicals as nodes, formulating an optimization problem to find an optimal set of radicals to represent the set of East Asian characters using maximum likelihood and minimum description length and solving the optimization problem for the optimal set of radicals. Another exemplary technique includes selecting an optimal set of radicals by using a general function that characterizes a radical with respect to other East Asian characters and a complex function that characterizes complexity of a radical.

摘要翻译： 描述了用于选择在概率东亚字符识别算法中使用的激进集合的示例性技术。 一个示例性的技术包括将分解规则应用于集合的每个东亚字符以生成逐行分割图，其中渐进分割图包括基数作为节点，制定优化问题以找到最佳的一组基团以表示东亚集 字符使用最大似然和最小描述长度，并解决优化问题的最佳组的自由基。 另一个示例性技术包括通过使用表征相对于其他东亚字符的基数的一般函数和表征激进的复杂度的复杂函数来选择最佳的自由基集合。

公开(公告)号：US07903877B2

公开(公告)日：2011-03-08

申请号：US11682722

申请日：2007-03-06

申请人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

发明人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/00 ,  G06K9/18

CPC分类号： G06K9/00879

摘要： Exemplary methods, systems, and computer-readable media for developing, training and/or using models for online handwriting recognition of characters are described. An exemplary method for building a trainable radical-based HMM for use in character recognition includes defining radical nodes, where a radical node represents a structural element of an character, and defining connection nodes, where a connection node represents a spatial relationship between two or more radicals. Such a method may include determining a number of paths in the radical-based HMM using subsequence direction histogram vector (SDHV) clustering and determining a number of states in the radical-based HMM using curvature scale space-based (CSS) corner detection.

摘要翻译： 描述用于开发，训练和/或使用用于字符的在线手写识别的模型的示例性方法，系统和计算机可读介质。 用于构建用于字符识别的可训练基于激进的基于HMM的示例性方法包括定义基本节点，其中基本节点表示字符的结构元素，并且定义连接节点，其中连接节点表示两个或更多个之间的空间关系 激进分子 这种方法可以包括使用子序列方向直方图向量（SDHV）聚类确定基于激进的HMM中的路径数量，并使用基于曲率空间的（CSS）角检测确定基于激进的HMM中的状态数。

公开(公告)号：US20070239432A1

公开(公告)日：2007-10-11

申请号：US11394809

申请日：2006-03-30

申请人： Frank Soong ,  Jian-Lai Zhou ,  Peng Liu

发明人： Frank Soong ,  Jian-Lai Zhou ,  Peng Liu

IPC分类号： G06F17/27

CPC分类号： G06F17/27

摘要： Multiple input modalities are selectively used by a user or process to prune a word graph. Pruning initiates rescoring in order to generate a new word graph with a revised best path.

摘要翻译： 用户或进程有选择地使用多种输入模式来修剪单词图形。 修剪开始拯救，以生成一个修改最佳路径的新字图。

公开(公告)号：US20070219797A1

公开(公告)日：2007-09-20

申请号：US11376803

申请日：2006-03-16

申请人： Peng Liu ,  Ye Tian ,  Jian-Lai Zhou ,  Frank Soong

发明人： Peng Liu ,  Ye Tian ,  Jian-Lai Zhou ,  Frank Soong

IPC分类号： G10L15/18

CPC分类号： G10L15/08 ,  G10L15/02 ,  G10L15/187 ,  G10L15/193

摘要： Speech recognition such as command and control speech recognition generally use a context free grammar to constrain the decoding process. Word or subword background model are constructed to repopulate dynamic hypothesis space, especially when word spareness is at issue. The background models can be later used in speech recognition. During speech recognition, background and conventional context free grammar decoding are used to measure confidence. The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.

摘要翻译： 诸如命令和控制语音识别之类的语音识别通常使用无上下文的语法来限制解码过程。 构建词或子词背景模型，以重新构建动态假设空间，特别是在词语空间问题时。 背景模型可以稍后用于语音识别。 在语音识别期间，使用背景和常规上下文无关语法解码来测量置信度。 上面的讨论仅用于一般背景信息，并不旨在用于帮助确定所要求保护的主题的范围。

公开(公告)号：US20060290656A1

公开(公告)日：2006-12-28

申请号：US11168279

申请日：2005-06-28

申请人： Frank Soong ,  Jian-Lai Zhou ,  Ye Tian

发明人： Frank Soong ,  Jian-Lai Zhou ,  Ye Tian

IPC分类号： G09G5/00

CPC分类号： G06F3/038 ,  G06F3/0237 ,  G06F3/04883 ,  G06F2203/0381

摘要： Input is received from at least two different input sources. Information from these sources are combined together to provide a result. In a particular example, input from one source corresponds to potential recognition candidates, and input from another source corresponds to other potential candidates. These candidates are combined together to select a result.

公开(公告)号：US09613450B2

公开(公告)日：2017-04-04

申请号：US13099387

申请日：2011-05-03

申请人： Lijuan Wang ,  Frank Soong ,  Qiang Huo ,  Zhengyou Zhang

发明人： Lijuan Wang ,  Frank Soong ,  Qiang Huo ,  Zhengyou Zhang

IPC分类号： G06T13/40 ,  G10L21/10

CPC分类号： G06T13/40 ,  G10L21/10 ,  G10L2021/105

摘要： Dynamic texture mapping is used to create a photorealistic three dimensional animation of an individual with facial features synchronized with desired speech. Audiovisual data of an individual reading a known script is obtained and stored in an audio library and an image library. The audiovisual data is processed to extract feature vectors used to train a statistical model. An input audio feature vector corresponding to desired speech with which the animation will be synchronized is provided. The statistical model is used to generate a trajectory of visual feature vectors that corresponds to the input audio feature vector. These visual feature vectors are used to identify a matching image sequence from the image library. The resulting sequence of images, concatenated from the image library, provides a photorealistic image sequence with facial features, such as lip movements, synchronized with the desired speech. This image sequence is applied to the three-dimensional model.

公开(公告)号：US20090099847A1

公开(公告)日：2009-04-16

申请号：US11973735

申请日：2007-10-10

申请人： Frank Soong ,  Lijuan Wang

发明人： Frank Soong ,  Lijuan Wang

IPC分类号： G10L11/00

CPC分类号： G10L2015/228

摘要： Detailed herein is a technology which, among other things, reduces errors introduced in recording and transcription data. In one approach to this technology, a method of detecting audio transcription errors is utilized. This method includes selected a focus unit, and selecting a context template corresponding to the focus unit. A hypothesis set is then determined, with reference to the context template and the focus unit. A probability is calculated corresponding to the focus unit, across the hypothesis set.

摘要翻译： 本文详细描述了一种技术，其中除了特别地减少在记录和转录数据中引入的错误。 在该技术的一种方法中，利用检测音频转录错误的方法。 该方法包括选择的焦点单元，以及选择与焦点单元对应的上下文模板。 然后参照上下文模板和焦点单元确定假设集合。 通过假设集合对应于焦点单位计算概率。

公开(公告)号：US20120280974A1

公开(公告)日：2012-11-08

申请号：US13099387

申请日：2011-05-03

申请人： Lijuan Wang ,  Frank Soong ,  Qiang Huo ,  Zhengyou Zhang

发明人： Lijuan Wang ,  Frank Soong ,  Qiang Huo ,  Zhengyou Zhang

IPC分类号： G06T13/40 ,  G06T15/00

CPC分类号： G06T13/40 ,  G10L21/10 ,  G10L2021/105

摘要： Dynamic texture mapping is used to create a photorealistic three dimensional animation of an individual with facial features synchronized with desired speech. Audiovisual data of an individual reading a known script is obtained and stored in an audio library and an image library. The audiovisual data is processed to extract feature vectors used to train a statistical model. An input audio feature vector corresponding to desired speech with which the animation will be synchronized is provided. The statistical model is used to generate a trajectory of visual feature vectors that corresponds to the input audio feature vector. These visual feature vectors are used to identify a matching image sequence from the image library. The resulting sequence of images, concatenated from the image library, provides a photorealistic image sequence with facial features, such as lip movements, synchronized with the desired speech. This image sequence is applied to the three-dimensional model.

摘要翻译： 动态纹理映射用于创建具有与期望语音同步的面部特征的个体的逼真的三维动画。 读取已知脚本的个人的视听数据被获取并存储在音频库和图像库中。 处理视听数据以提取用于训练统计模型的特征向量。 提供对应于动画将被同步的期望语音的输入音频特征向量。 统计模型用于生成对应于输入音频特征向量的视觉特征向量的轨迹。 这些视觉特征向量用于识别来自图像库的匹配图像序列。 从图像库连接的所得到的图像序列提供具有与所需语音同步的面部特征（例如唇部移动）的照片写实图像序列。 该图像序列应用于三维模型。