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    • 3. 发明申请
    • Online Handwriting Expression Recognition
    • 在线手写表达识别
    • US20090245646A1
    • 2009-10-01
    • US12058506
    • 2008-03-28
    • Yu ShiFrank Kao-Ping Soong
    • Yu ShiFrank Kao-Ping Soong
    • G06K9/78
    • G06K9/00422G06K9/00879
    • One way of recognizing online handwritten mathematical expressions is to use a one-pass dynamic programming based symbol decoding generation algorithm. This method embeds segmentation into symbol identification to form a unified framework for symbol recognition. Along with decoding, a symbol graph is produced. Besides accurately recognizing handwritten mathematical expressions, this method can produce high quality symbol graphs. This method uses six knowledge source models to help search for possible symbol hypotheses during the decoding process. Here, knowledge source exponential weights and a symbol insertion penalty are used to weigh the various knowledge source model probabilities to increase accuracy.
    • 识别在线手写数学表达式的一种方法是使用基于单程动态规划的符号解码生成算法。 该方法将分段嵌入符号识别中,形成符号识别的统一框架。 随着解码,产生符号图。 除了精确地识别手写数学表达,这种方法可以产生高质量的符号图。 该方法使用六种知识源模型来帮助在解码过程中搜索可能的符号假设。 这里,知识源指数权重和符号插入罚分用于权衡各种知识源模型概率以提高准确性。
    • 4. 发明申请
    • Segment Sequence-Based Handwritten Expression Recognition
    • 基于片段序列的手写表达识别
    • US20100166314A1
    • 2010-07-01
    • US12346376
    • 2008-12-30
    • Yu ShiFrank Kao-Ping Soong
    • Yu ShiFrank Kao-Ping Soong
    • G06K9/00
    • G06K9/00422G06K9/6296
    • Methods and apparatuses for generating, by a computing device configured to interpret a handwritten expression, a symbol graph to represent strokes associated with the handwritten expression, are described herein. The symbol graph may include nodes, each node corresponding to a combination of a stroke and a candidate symbol for that stroke. The computing device may also generate a segment graph based on the symbol graph by combining nodes associated with a same stroke if strokes of their preceding nodes are the same. Also the computing device may perform a structure analysis on at least a subset of segment sequences represented by the segment graph to determine hypotheses for the handwritten expression. In other embodiments, rather than generate a segment graph, the computing device may determine segment sequences by selecting a number of symbol sequences from the symbol graph and combining symbol sequences having the same segmentation.
    • 本文描述了通过被配置为解读手写表达式的计算设备来生成表示与手写表达式相关联的笔画的符号图形的方法和装置。 符号图可以包括节点,每个节点对应于笔划的组合和该笔划的候选符号。 如果其前面的节点的笔划相同,则计算设备还可以通过组合与相同笔划相关联的节点来基于该符号图来生成片段图。 此外,计算设备还可以对由片段图表示的段序列的至少一个子集进行结构分析,以确定手写表达式的假设。 在其他实施例中,计算设备可以通过从符号图中选择符号序列的数目并组合具有相同分割的符号序列来确定段序列而不是生成段图。
    • 8. 发明申请
    • EVALUATING TEXT-TO-SPEECH INTELLIGIBILITY USING TEMPLATE CONSTRAINED GENERALIZED POSTERIOR PROBABILITY
    • 使用模板约束的一般化后验概率评估文本到语音智能
    • US20140025381A1
    • 2014-01-23
    • US13554480
    • 2012-07-20
    • Linfang WangYan TengLijuan WangFrank Kao-Ping SoongZhe GengWilliam Brad WallerMark Tillman Hanson
    • Linfang WangYan TengLijuan WangFrank Kao-Ping SoongZhe GengWilliam Brad WallerMark Tillman Hanson
    • G10L13/08
    • G10L25/69G10L13/00
    • Instead of relying on humans to subjectively evaluate speech intelligibility of a subject, a system objectively evaluates the speech intelligibility. The system receives speech input and calculates confidence scores at multiple different levels using a Template Constrained Generalized Posterior Probability algorithm. One or multiple intelligibility classifiers are utilized to classify the desired entities on an intelligibility scale. A specific intelligibility classifier utilizes features such as the various confidence scores. The scale of the intelligibility classification can be adjusted to suit the application scenario. Based on the confidence score distributions and the intelligibility classification results at multiple levels an overall objective intelligibility score is calculated. The objective intelligibility scores can be used to rank different subjects or systems being assessed according to their intelligibility levels. The speech that is below a predetermined intelligibility (e.g. utterances with low confidence scores and most severe intelligibility issues) can be automatically selected for further analysis.
    • 系统客观地评估语言的可懂度,而不是依靠人类来主观地评估一个主题的语音清晰度。 系统接收语音输入,并使用模板约束广义后验概率算法在多个不同级别计算置信度分数。 一个或多个可理解性分类器用于在可懂度量表上分类所需实体。 特定的清晰度分类器利用各种置信度分数等特征。 可以调整可懂度分类的规模,以适应应用场景。 基于多个级别的置信度分数和可理解性分类结果,计算出总目标可懂度分数。 客观可理解性分数可用于根据其可理解性级别对待评估的不同科目或系统进行排名。 可以自动选择低于预定清晰度(例如具有低置信度得分和最严重的可理解性问题的话语)的语音用于进一步分析。
    • 10. 发明申请
    • Line Spectrum pair density modeling for speech applications
    • 用于语音应用的线谱对密度建模
    • US20080195381A1
    • 2008-08-14
    • US11704522
    • 2007-02-09
    • Frank Kao-Ping SoongYao Qian
    • Frank Kao-Ping SoongYao Qian
    • G10L11/00
    • G10L25/48
    • Novel techniques for providing superior performance and sound quality in speech applications, such as speech synthesis, speech coding, and automatic speech recognition, are hereby disclosed. In one illustrative embodiment, a method includes modeling a speech signal with parameters comprising line spectrum pairs. Density parameters are provided based on the density of the line spectrum pairs. A speech application output, such as synthesized speech, is provided based at least in part on the line spectrum pair density parameters. The line spectrum pair density parameters use computing resources efficiently while providing improved performance and sound quality in the speech application output.
    • 特此公开了用于在诸如语音合成,语音编码和自动语音识别等语音应用中提供卓越性能和声音质量的新技术。 在一个说明性实施例中,一种方法包括用包括线谱对的参数对语音信号进行建模。 基于线谱对的密度提供密度参数。 至少部分地基于线谱对密度参数来提供诸如合成语音的语音应用输出。 线谱对密度参数有效利用计算资源,同时在语音应用输出中提供改进的性能和声音质量。