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1. 发明授权

US07680657B2 Auto segmentation based partitioning and clustering approach to robust endpointing 失效
标题翻译：基于自动分割的分区和聚类方法进行鲁棒终点
公开(公告)号：US07680657B2
公开(公告)日：2010-03-16
申请号：US11504280
申请日：2006-08-15
申请人： Yu Shi , Frank Kao-ping Soong , Jian-Iai Zhou
发明人： Yu Shi , Frank Kao-ping Soong , Jian-Iai Zhou
IPC分类号： G10L15/04 , G10L15/20 , G10L15/00
CPC分类号： G10L25/87
摘要： Possible segmentations for an audio signal are scored based on distortions for feature vectors of the audio signal and the total number of segments in the segmentation. The scores are used to select a segmentation and the selected segmentation is used to identify a starting point and an ending point for a speech signal in the audio signal.
摘要翻译：基于音频信号的特征向量的失真和分段中的段的总数，对音频信号进行可能的分割。分数用于选择分割，并且所选择的分割用于识别音频信号中的语音信号的起始点和终点。

2. 发明授权

US07885456B2 Symbol graph generation in handwritten mathematical expression recognition 有权
标题翻译：手写数学表达式识别中的符号图生成
公开(公告)号：US07885456B2
公开(公告)日：2011-02-08
申请号：US11693299
申请日：2007-03-29
申请人： Yu Shi , Frank Kao-Ping Soong , Jian-Iai Zhou , Dongmei Zhang, legal representative
发明人： Yu Shi , Frank Kao-Ping Soong , Jian-Iai Zhou
IPC分类号： G06K9/00
CPC分类号： G06K9/6296 , G06K2209/01
摘要： A forward pass through a sequence of strokes representing a handwritten equation is performed from the first stroke to the last stroke in the sequence. At each stroke, a path score is determined for a plurality of symbol-relation pairs that each represents a symbol and its spatial relation to a predecessor symbol. A symbol graph having nodes and links is constructed by backtracking through the strokes from the last stroke to the first stroke and assigning scores to the links based on the path scores for the symbol-relation pairs. The symbol graph is used to recognize a mathematical expression based in part on the scores for the links and the mathematical expression is stored.
摘要翻译：从序列中的第一行程到最后一个行程执行表示手写方程的笔画序列的向前传递。在每个笔划处，确定多个符号 - 关系对的路径分数，每个符号 - 关系对都表示符号及其与前身符号的空间关系。具有节点和链接的符号图是通过从最后笔划到第一笔划的笔画回溯构成的，并且基于符号 - 关系对的路径得分将分数分配给链接。符号图用于部分地基于链接的分数来识别数学表达式，并存储数学表达式。

3. 发明申请

US20080243503A1 MINIMUM DIVERGENCE BASED DISCRIMINATIVE TRAINING FOR PATTERN RECOGNITION 审中-公开
标题翻译：最小的基于分歧的歧视识别训练
公开(公告)号：US20080243503A1
公开(公告)日：2008-10-02
申请号：US11694375
申请日：2007-03-30
申请人： Frank Kao-Ping Soong , Peng Liu , Jian-Iai Zhou , Dongmei Zhang
发明人： Frank Kao-Ping Soong , Peng Liu , Jian-Iai Zhou , Dongmei Zhang
IPC分类号： G10L15/06
CPC分类号： G10L15/063
摘要： A method of providing discriminative training of a speech recognition unit is discussed. The method includes receiving an acoustic indication of an utterance having a hypothesis space and comparing the hypothesis space against a reference. The method measures the Kullback-Leibler Divergence (KLD) between the reference and the hypothesis space to adjust the reference and stores the adjusted reference on a tangible storage medium.
摘要翻译：讨论了提供语音识别单元的辨别性训练的方法。该方法包括接收具有假设空间的话语的声学指示，并将假设空间与参考值进行比较。该方法测量参考和假设空间之间的Kullback-Leibler发散（KLD）以调整参考，并将调整的参考存储在有形存储介质上。

4. 发明授权

US07680664B2 Parsimonious modeling by non-uniform kernel allocation 有权
标题翻译：通过非均匀内核分配进行建模
公开(公告)号：US07680664B2
公开(公告)日：2010-03-16
申请号：US11505206
申请日：2006-08-16
申请人： Peng Liu , Jian-Iai Zhou , Frank Kao-ping Soong
发明人： Peng Liu , Jian-Iai Zhou , Frank Kao-ping Soong
IPC分类号： G10L15/14 , G10L15/28
CPC分类号： G06K9/6297 , G10L15/14 , G10L15/144
摘要： A multi-state pattern recognition model with non-uniform kernel allocation is formed by setting a number of states for a multi-state pattern recognition model and assigning different numbers of kernels to different states. The kernels are then trained using training data to form the multi-state pattern recognition model.
摘要翻译：通过为多状态模式识别模型设置多个状态并将不同数量的内核分配给不同的状态，形成具有不均匀内核分配的多状态模式识别模型。然后使用训练数据训练内核以形成多状态模式识别模型。

5. 发明授权

US07617104B2 Method of speech recognition using hidden trajectory Hidden Markov Models 失效
标题翻译：使用隐藏轨迹隐马尔可夫模型的语音识别方法
公开(公告)号：US07617104B2
公开(公告)日：2009-11-10
申请号：US10348192
申请日：2003-01-21
申请人： Li Deng , Jian-Iai Zhou , Frank Torsten Bernd Seide
发明人： Li Deng , Jian-Iai Zhou , Frank Torsten Bernd Seide
IPC分类号： G10L15/14
CPC分类号： G10L15/142
摘要： A method of speech recognition is provided that determines a production-related value, vocal-tract resonance frequencies in particular, for a state at a particular frame based on the production-related values associated with two preceding frames using a recursion. The production-related value is used to determine a probability distribution of the observed feature vector for the state. A probability for an observed value received for the frame is then determined from the probability distribution. Under one embodiment, the production-related value is determined using a noise-free recursive definition for the value. Use of the recursion substantially improves the decoding speed. When the decoding algorithm is applied to training data with known phonetic transcripts, forced alignment is created which improves the phone segmentation obtained from the prior art.
摘要翻译：提供了一种语音识别方法，其基于与使用递归的两个先前帧相关联的生产相关值来确定特定帧处的状态的特定生产相关值，声道共振频率。生产相关值用于确定观察到的状态特征向量的概率分布。然后从概率分布确定为帧接收的观测值的概率。在一个实施例中，使用针对该值的无噪声递归定义来确定生产相关值。使用递归大大提高了解码速度。当解码算法应用于具有已知语音抄本的训练数据时，产生强制对准，其改善了从现有技术获得的电话分割。

6. 发明授权

US07050975B2 Method of speech recognition using time-dependent interpolation and hidden dynamic value classes 有权
标题翻译：使用时间依赖插值和隐藏动态值类的语音识别方法
公开(公告)号：US07050975B2
公开(公告)日：2006-05-23
申请号：US10267522
申请日：2002-10-09
申请人： Li Deng , Jian-Iai Zhou , Frank Torsten Bernd Seide , Asela J. R. Gunawardana , Hagai Attias , Alejandro Acero , Xuedong Huang
发明人： Li Deng , Jian-Iai Zhou , Frank Torsten Bernd Seide , Asela J. R. Gunawardana , Hagai Attias , Alejandro Acero , Xuedong Huang
IPC分类号： G10L15/14
CPC分类号： G10L15/12 , G10L2015/025
摘要： A method of speech recognition is provided that identifies a production-related dynamics value by performing a linear interpolation between a production-related dynamics value at a previous time and a production-related target using a time-dependent interpolation weight. The hidden production-related dynamics value is used to compute a predicted value that is compared to an observed value of acoustics to determine the likelihood of the observed acoustics given a sequence of hidden phonological units. In some embodiments, the production-related dynamics value at the previous time is selected from a set of continuous values. In addition, the likelihood of the observed acoustics given a sequence of hidden phonological units is combined with a score associated with a discrete class of production-related dynamic values at the previous time to determine a score for a current phonological state.
摘要翻译：提供了一种语音识别方法，其通过使用时间相关的内插权重在前一时间通过执行生产相关动态值与生产相关目标之间的线性插值来识别生产相关动态值。隐藏的生产相关动态值用于计算与观测值相比较的预测值，以确定给定隐藏语音单元序列的观测声学的可能性。在一些实施例中，从一组连续值中选择先前时间的生产相关动态值。另外，给出隐藏语音单元序列的观测声学的可能性与前一时刻与离散类别的生产相关动态值相关联的得分组合，以确定当前语音状态的得分。

7. 发明申请

US20060085191A1 Method of speech recognition using time-dependent interpolation and hidden dynamic value classes 有权
标题翻译：使用时间依赖插值和隐藏动态值类的语音识别方法
公开(公告)号：US20060085191A1
公开(公告)日：2006-04-20
申请号：US11294858
申请日：2005-12-06
申请人： Li Deng , Jian-Iai Zhou , Frank Seide , Asela Gunawardana , Hagai Attias , Alejandro Acero , Xuedong Huang
发明人： Li Deng , Jian-Iai Zhou , Frank Seide , Asela Gunawardana , Hagai Attias , Alejandro Acero , Xuedong Huang
IPC分类号： G10L15/14
CPC分类号： G10L15/12 , G10L2015/025
摘要： A speech signal is decoded by determining a production-related value for a current state based on an optimal production-related value at the end of a preceding state, the optimal production-related value being selected from a set of continuous values. The production-related value is used to determine a likelihood of a phone being represented by a set of observation vectors that are aligned with a path between the preceding state and the current state. The likelihood of the phone is combined with a score from the preceding state to determine a score for the current state, the score from the preceding state being associated with a discrete class of production-related values wherein the class matches the class of the optimal production-related value.
摘要翻译：通过基于在先前状态结束时的最佳生产相关值来确定当前状态的生产相关值来解码语音信号，从一组连续值中选择最佳生产相关值。生产相关值用于确定电话由与先前状态和当前状态之间的路径对准的一组观察向量表示的可能性。电话的可能性与来自前述状态的得分组合以确定当前状态的分数，来自前一状态的分数与生产相关值的离散类相关联，其中该类与最佳生产类别匹配相关价值。

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