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    Sequence classification for machine translation
    1.
    发明申请
    Sequence classification for machine translation 失效
    机器翻译序列分类

    公开(公告)号：US20080162111A1

    公开(公告)日：2008-07-03

    申请号：US11647080

    申请日：2006-12-28

    申请人： Srinivas Bangalore ,  Patrick Haffner ,  Stephan Kanthak

    发明人： Srinivas Bangalore ,  Patrick Haffner ,  Stephan Kanthak

    IPC分类号： G06F17/28

    CPC分类号： G06F17/2818

    摘要： Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word. The weights comprising the vectors are associated with respective ones of the features; each weight is a measure of the extent to which the presence of that feature for the source word makes it more probable that the target word in question is the correct one.

    摘要翻译： 使用独立假设进行序列分类，如自然语言句子的翻译。 独立性假设是将源语句正确翻译成特定目标句子词的概率与句子中其他单词的翻译无关的假设。 尽管这种假设不是正确的，但仍然会实现高水平的字翻译精度。 特别地，歧视性训练被用于基于训练句子中相应源词的一组特征来开发每个目标词汇词的模型，其中至少一个与源词的上下文有关的特征。 每个模型包括对应的目标词汇单词的权重向量。 包括向量的权重与相应的特征相关联; 每个权重是衡量源字符的该特征的存在程度使得所述目标词更可能是正确的。

    Discriminative training of models for sequence classification
    2.
    发明申请
    Discriminative training of models for sequence classification 审中-公开
    序列分类模型的辨别性训练

    公开(公告)号：US20080162117A1

    公开(公告)日：2008-07-03

    申请号：US11646983

    申请日：2006-12-28

    申请人： Srinivas Bangalore ,  Patrick Haffner ,  Stephan Kanthak

    发明人： Srinivas Bangalore ,  Patrick Haffner ,  Stephan Kanthak

    IPC分类号： G06F17/21

    CPC分类号： G06F17/2818

    摘要： Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word. The weights comprising the vectors are associated with respective ones of the features; each weight is a measure of the extent to which the presence of that feature for the source word makes it more probable that the target word in question is the correct one.

    摘要翻译： 使用独立假设进行序列分类，如自然语言句子的翻译。 独立性假设是将源语句正确翻译成特定目标句子词的概率与句子中其他单词的翻译无关的假设。 尽管这种假设不是正确的，但仍然会实现高水平的字翻译精度。 特别地，歧视性训练被用于基于训练句子中相应源词的一组特征来开发每个目标词汇词的模型，其中至少一个与源词的上下文有关的特征。 每个模型包括对应的目标词汇单词的权重向量。 包括向量的权重与相应的特征相关联; 每个权重是衡量源字符的该特征的存在程度使得所述目标词更可能是正确的。

    Sequence classification for machine translation
    4.
    发明授权
    Sequence classification for machine translation 失效
    机器翻译序列分类

    公开(公告)号：US07783473B2

    公开(公告)日：2010-08-24

    申请号：US11647080

    申请日：2006-12-28

    申请人： Srinivas Bangalore ,  Patrick Haffner ,  Stephan Kanthak

    发明人： Srinivas Bangalore ,  Patrick Haffner ,  Stephan Kanthak

    IPC分类号： G06F17/28 ,  G10L21/00

    CPC分类号： G06F17/2818

    摘要： Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word. The weights comprising the vectors are associated with respective ones of the features; each weight is a measure of the extent to which the presence of that feature for the source word makes it more probable that the target word in question is the correct one.

    摘要翻译： 使用独立假设进行序列分类，如自然语言句子的翻译。 独立性假设是将源语句正确翻译成特定目标句子词的概率与句子中其他单词的翻译无关的假设。 尽管这种假设不是正确的，但仍然会实现高水平的字翻译精度。 特别地，歧视性训练被用于基于训练句子中相应源词的一组特征来开发每个目标词汇词的模型，其中至少一个与源词的上下文有关的特征。 每个模型包括对应的目标词汇单词的权重向量。 包括向量的权重与相应的特征相关联; 每个权重是衡量源字符的该特征的存在程度使得所述目标词更可能是正确的。

    System and method for combining speech recognition outputs from a plurality of domain-specific speech recognizers via machine learning
    5.
    发明授权
    System and method for combining speech recognition outputs from a plurality of domain-specific speech recognizers via machine learning 有权
    用于通过机器学习来组合来自多个领域特定语音识别器的语音识别输出的系统和方法

    公开(公告)号：US08812321B2

    公开(公告)日：2014-08-19

    申请号：US12895359

    申请日：2010-09-30

    申请人： Mazin Gilbert ,  Srinivas Bangalore ,  Patrick Haffner ,  Robert Bell

    发明人： Mazin Gilbert ,  Srinivas Bangalore ,  Patrick Haffner ,  Robert Bell

    IPC分类号： G10L15/08 ,  G10L15/32

    CPC分类号： G10L15/32 ,  G10L15/063 ,  G10L15/26 ,  G10L2015/0638

    摘要： Disclosed herein are systems, methods and non-transitory computer-readable media for performing speech recognition across different applications or environments without model customization or prior knowledge of the domain of the received speech. The disclosure includes recognizing received speech with a collection of domain-specific speech recognizers, determining a speech recognition confidence for each of the speech recognition outputs, selecting speech recognition candidates based on a respective speech recognition confidence for each speech recognition output, and combining selected speech recognition candidates to generate text based on the combination.

    摘要翻译： 本文公开了用于在不需要模型定制或接收到的语音的领域的先前知识的情况下在不同的应用或环境上执行语音识别的系统，方法和非暂时的计算机可读介质。 该公开内容包括：利用特定领域的语音识别器的集合来识别接收的语音，为每个语音识别输出确定语音识别置信度，基于每个语音识别输出的相应的语音识别置信度选择语音识别候选，以及组合所选语音 识别候选人基于组合生成文本。

    SYSTEM AND METHOD FOR OPEN SPEECH RECOGNITION
    6.
    发明申请
    SYSTEM AND METHOD FOR OPEN SPEECH RECOGNITION 有权
    用于开放语音识别的系统和方法

    公开(公告)号：US20120084086A1

    公开(公告)日：2012-04-05

    申请号：US12895359

    申请日：2010-09-30

    申请人： Mazin GILBERT ,  Srinivas Bangalore ,  Patrick Haffner ,  Robert Bell

    发明人： Mazin GILBERT ,  Srinivas Bangalore ,  Patrick Haffner ,  Robert Bell

    IPC分类号： G10L15/26

    CPC分类号： G10L15/32 ,  G10L15/063 ,  G10L15/26 ,  G10L2015/0638

    摘要： Disclosed herein are systems, methods and non-transitory computer-readable media for performing speech recognition across different applications or environments without model customization or prior knowledge of the domain of the received speech. The disclosure includes recognizing received speech with a collection of domain-specific speech recognizers, determining a speech recognition confidence for each of the speech recognition outputs, selecting speech recognition candidates based on a respective speech recognition confidence for each speech recognition output, and combining selected speech recognition candidates to generate text based on the combination.

    摘要翻译： 本文公开了用于在不需要模型定制或接收到的语音的领域的先前知识的情况下在不同的应用或环境上执行语音识别的系统，方法和非暂时的计算机可读介质。 该公开内容包括：利用特定领域的语音识别器的集合来识别接收的语音，为每个语音识别输出确定语音识别置信度，基于每个语音识别输出的相应语音识别置信度选择语音识别候选，以及组合所选语音 识别候选人基于组合生成文本。

    On-Demand Language Translation For Television Programs
    7.
    发明申请
    On-Demand Language Translation For Television Programs 有权
    电视节目的按需语言翻译

    公开(公告)号：US20070244688A1

    公开(公告)日：2007-10-18

    申请号：US11279852

    申请日：2006-04-14

    申请人： Srinivas Bangalore ,  David Gibbon ,  Mazin Gilbert ,  Patrick Haffner ,  Zhu Liu ,  Behzad Shahraray

    发明人： Srinivas Bangalore ,  David Gibbon ,  Mazin Gilbert ,  Patrick Haffner ,  Zhu Liu ,  Behzad Shahraray

    IPC分类号： G06F17/28

    CPC分类号： H04N21/4856 ,  G06F17/289 ,  G10L21/10 ,  H04N7/0885 ,  H04N7/17336 ,  H04N21/2355 ,  H04N21/2543 ,  H04N21/25435 ,  H04N21/4355 ,  H04N21/440236 ,  H04N21/4882 ,  H04N21/4884 ,  H04N21/8193

    摘要： A method, a system and a machine-readable medium are provided for an on demand translation service. A translation module including at least one language pair module for translating a source language to a target language may be made available for use by a subscriber. The subscriber may be charged a fee for use of the requested on demand translation service or may be provided use of the on demand translation service for free in exchange for displaying commercial messages to the subscriber. A video signal may be received including information in the source language, which may be obtained as text from the video signal and may be translated from the source language to the target language by use of the translation module. Translated information, based on the translated text, may be added into the received video signal. The video signal including the translated information in the target language may be sent to a display device.

    摘要翻译： 为按需翻译服务提供方法，系统和机器可读介质。 包括用于将源语言翻译成目标语言的至少一个语言对模块的翻译模块可以被用户使用。 用户可能会收取使用所请求的按需翻译服务的费用，或者可以免费使用按需翻译服务，以便向用户显示商业消息。 可以接收包括源语言的信息的视频信号，其可以从视频信号获取为文本，并且可以通过使用翻译模块从源语言转换为目标语言。 基于翻译文本的翻译信息可以被添加到接收的视频信号中。 可以将包含目标语言的翻译信息的视频信号发送到显示装置。