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1.发明授权Unsupervised and active learning in automatic speech recognition for call classification 有权无监督和主动学习自动语音识别呼叫分类公开(公告)号：US08818808B2
公开(公告)日：2014-08-26
申请号：US11063910
申请日：2005-02-23
IPC分类号： G10L15/06
CPC分类号： G10L15/063 , G10L15/07 , G10L15/18 , G10L15/26 , G10L2015/0638
摘要： Utterance data that includes at least a small amount of manually transcribed data is provided. Automatic speech recognition is performed on ones of the utterance data not having a corresponding manual transcription to produce automatically transcribed utterances. A model is trained using all of the manually transcribed data and the automatically transcribed utterances. A predetermined number of utterances not having a corresponding manual transcription are intelligently selected and manually transcribed. Ones of the automatically transcribed data as well as ones having a corresponding manual transcription are labeled. In another aspect of the invention, audio data is mined from at least one source, and a language model is trained for call classification from the mined audio data to produce a language model.
摘要翻译： 提供了至少包含少量手动转录数据的语音数据。 对没有相应的手动转录的话语数据中的一个进行自动语音识别以产生自动转录的话语。 使用所有手动转录数据和自动转录的话语训练模型。 智能地选择并且手动地转录预定数量的不具有对应的手动转录的话语。 自动转录的数据以及具有相应手动转录的数据的标签。 在本发明的另一方面，音频数据从至少一个源开始，并且语言模型被训练用于从所开采的音频数据进行呼叫分类以产生语言模型。
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公开(公告)号：US07292976B1
公开(公告)日：2007-11-06
申请号：US10447888
申请日：2003-05-29
CPC分类号： G06F17/2775 , G06F17/277 , G10L2015/0631
摘要： A large amount of human labor is required to transcribe and annotate a training corpus that is needed to create and update models for automatic speech recognition (ASR) and spoken language understanding (SLU). Active learning enables a reduction in the amount of transcribed and annotated data required to train ASR and SLU models. In one aspect of the present invention, an active learning ASR process and active learning SLU process are coupled, thereby enabling further efficiencies to be gained relative to a process that maintains an isolation of data in both the ASR and SLU domains.
摘要翻译： 需要大量的人力劳动来转录和注释创建和更新自动语音识别（ASR）和语言理解（SLU）模型所需的训练语料库。 主动学习可以减少训练ASR和SLU模型所需的转录和注释数据量。 在本发明的一个方面，耦合主动学习ASR过程和主动学习SLU过程，从而相对于维持ASR和SLU域中的数据隔离的过程而获得进一步的效率。
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公开(公告)号：US20080270130A1
公开(公告)日：2008-10-30
申请号：US12165755
申请日：2008-07-01
申请人： Tirso M. Alonso , Ilana Bromberg , Dilek Z. Hakkani-Tur , Barbara B. Hollister , Mazin G. Rahim , Giuseppe Riccardi , Lawrence Lyon Rose , Daniel Leon Stern , Gokhan Tur , James M. Wilson
发明人： Tirso M. Alonso , Ilana Bromberg , Dilek Z. Hakkani-Tur , Barbara B. Hollister , Mazin G. Rahim , Giuseppe Riccardi , Lawrence Lyon Rose , Daniel Leon Stern , Gokhan Tur , James M. Wilson
IPC分类号： G10L15/00
CPC分类号： G10L15/19 , G10L15/063 , G10L15/183 , H04M3/4936
摘要： Systems and methods for annotating speech data. The present invention reduces the time required to annotate speech data by selecting utterances for annotation that will be of greatest benefit. A selection module uses speech models, including speech recognition models and spoken language understanding models, to identify utterances that should be annotated based on criteria such as confidence scores generated by the models. These utterances are placed in an annotation list along with a type of annotation to be performed for the utterances and an order in which the annotation should proceed. The utterances in the annotation list can be annotated for speech recognition purposes, spoken language understanding purposes, labeling purposes, etc. The selection module can also select utterances for annotation based on previously annotated speech data and deficiencies in the various models.
摘要翻译： 用于注释语音数据的系统和方法。 本发明通过选择最有益的用于注释的话语来减少注释语音数据所需的时间。 选择模块使用包括语音识别模型和语言理解模型在内的语音模型来基于诸如由模型产生的置信度得分的标准来识别应当注释的话语。 这些话语被放置在注释列表中，以及要为语句执行的注释类型以及注释应该继续执行的顺序。 注释列表中的话语可以被注释用于语音识别目的，语言理解目的，标签目的等。选择模块还可以基于先前注释的语音数据和各种模型中的缺陷来选择用于注释的话语。
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4.发明授权公开(公告)号：US07860713B2
公开(公告)日：2010-12-28
申请号：US12165755
申请日：2008-07-01
申请人： Tirso M. Alonso , Ilana Bromberg , Dilek Z. Hakkani-Tur , Barbara B. Hollister , Mazin G. Rahim , Giuseppe Riccardi , Lawrence Lyon Rose , Daniel Leon Stern , Gokhan Tur , James M. Wilson
发明人： Tirso M. Alonso , Ilana Bromberg , Dilek Z. Hakkani-Tur , Barbara B. Hollister , Mazin G. Rahim , Giuseppe Riccardi , Lawrence Lyon Rose , Daniel Leon Stern , Gokhan Tur , James M. Wilson
CPC分类号： G10L15/19 , G10L15/063 , G10L15/183 , H04M3/4936
摘要： Systems and methods for annotating speech data. The present invention reduces the time required to annotate speech data by selecting utterances for annotation that will be of greatest benefit. A selection module uses speech models, including speech recognition models and spoken language understanding models, to identify utterances that should be annotated based on criteria such as confidence scores generated by the models. These utterances are placed in an annotation list along with a type of annotation to be performed for the utterances and an order in which the annotation should proceed. The utterances in the annotation list can be annotated for speech recognition purposes, spoken language understanding purposes, labeling purposes, etc. The selection module can also select utterances for annotation based on previously annotated speech data and deficiencies in the various models.
摘要翻译： 用于注释语音数据的系统和方法。 本发明通过选择最有益的用于注释的话语来减少注释语音数据所需的时间。 选择模块使用包括语音识别模型和语言理解模型在内的语音模型来基于诸如由模型产生的置信度得分的标准来识别应当注释的话语。 这些话语被放置在注释列表中，以及要为语句执行的注释类型以及注释应该继续执行的顺序。 注释列表中的话语可以被注释用于语音识别目的，语言理解目的，标签目的等。选择模块还可以基于先前注释的语音数据和各种模型中的缺陷来选择用于注释的话语。
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5.发明授权公开(公告)号：US07412383B1
公开(公告)日：2008-08-12
申请号：US10407965
申请日：2003-04-04
申请人： Tirso M. Alonso , Ilana Bromberg , Dilek Z. Hakkani-Tur , Barbara B. Hollister , Mazin G. Rahim , Giuseppe Riccardi , Lawrence Lyon Rose , Daniel Leon Stern , Gokhan Tur , James M. Wilson
发明人： Tirso M. Alonso , Ilana Bromberg , Dilek Z. Hakkani-Tur , Barbara B. Hollister , Mazin G. Rahim , Giuseppe Riccardi , Lawrence Lyon Rose , Daniel Leon Stern , Gokhan Tur , James M. Wilson
CPC分类号： G10L15/19 , G10L15/063 , G10L15/183 , H04M3/4936
摘要： Systems and methods for annotating speech data. The present invention reduces the time required to annotate speech data by selecting utterances for annotation that will be of greatest benefit. A selection module uses speech models, including speech recognition models and spoken language understanding models, to identify utterances that should be annotated based on criteria such as confidence scores generated by the models. These utterances are placed in an annotation list along with a type of annotation to be performed for the utterances and an order in which the annotation should proceed. The utterances in the annotation list can be annotated for speech recognition purposes, spoken language understanding purposes, labeling purposes, etc. The selection module can also select utterances for annotation based on previously annotated speech data and deficiencies in the various models.
摘要翻译： 用于注释语音数据的系统和方法。 本发明通过选择最有益的用于注释的话语来减少注释语音数据所需的时间。 选择模块使用包括语音识别模型和语言理解模型在内的语音模型来基于诸如由模型产生的置信度得分的标准来识别应当注释的话语。 这些话语被放置在注释列表中，以及要为语句执行的注释类型以及注释应该继续执行的顺序。 注释列表中的话语可以被注释用于语音识别目的，语言理解目的，标签目的等。选择模块还可以基于先前注释的语音数据和各种模型中的缺陷来选择用于注释的话语。
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6.发明授权System and method of providing an automated data-collection in spoken dialog systems 有权在口头对话系统中提供自动数据收集的系统和方法公开(公告)号：US08185399B2
公开(公告)日：2012-05-22
申请号：US11029798
申请日：2005-01-05
CPC分类号： G10L15/063 , G10L15/183 , G10L15/22
摘要： The invention relates to a system and method for gathering data for use in a spoken dialog system. An aspect of the invention is generally referred to as an automated hidden human that performs data collection automatically at the beginning of a conversation with a user in a spoken dialog system. The method comprises presenting an initial prompt to a user, recognizing a received user utterance using an automatic speech recognition engine and classifying the recognized user utterance using a spoken language understanding module. If the recognized user utterance is not understood or classifiable to a predetermined acceptance threshold, then the method re-prompts the user. If the recognized user utterance is not classifiable to a predetermined rejection threshold, then the method transfers the user to a human as this may imply a task-specific utterance. The received and classified user utterance is then used for training the spoken dialog system.
摘要翻译： 本发明涉及一种用于收集在口头对话系统中使用的数据的系统和方法。 本发明的一个方面通常被称为在与对话系统中的用户的对话开始时自动执行数据收集的自动隐藏人。 该方法包括向用户呈现初始提示，使用自动语音识别引擎识别接收到的用户话语，并使用口语理解模块对所识别的用户话语进行分类。 如果识别的用户话语不能被理解或可被分类到预定的接受阈值，则该方法重新提示用户。 如果识别的用户话语不能被分类为预定的拒绝阈值，则该方法将用户转移给人，因为这可能意味着任务特定的话语。 然后，接收和分类的用户话语用于训练口语对话系统。
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公开(公告)号：US07949525B2
公开(公告)日：2011-05-24
申请号：US12485103
申请日：2009-06-16
申请人： Dilek Z. Hakkani-Tur , Mazin G. Rahim , Gokhan Tur
发明人： Dilek Z. Hakkani-Tur , Mazin G. Rahim , Gokhan Tur
CPC分类号： G10L15/1822
摘要： A spoken language understanding method and system are provided. The method includes classifying a set of labeled candidate utterances based on a previously trained classifier, generating classification types for each candidate utterance, receiving confidence scores for the classification types from the trained classifier, sorting the classified utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, and rechecking candidate utterances according to the analysis. The system includes modules configured to control a processor in the system to perform the steps of the method.
摘要翻译： 提供口语理解方法和系统。 该方法包括基于先前训练的分类器对一组标记的候选话语进行分类，为每个候选语音生成分类类型，从训练分类器接收分类类型的置信度分数， 每个候选话语与候选话语的相应标签相比较，并且根据分析重新检查候选话语。 该系统包括被配置为控制系统中的处理器以执行该方法的步骤的模块。
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8.发明申请SYSTEM AND METHOD OF PROVIDING AN AUTOMATED DATA-COLLECTION IN SPOKEN DIALOG SYSTEMS 有权系统和方法提供自动数据收集在风扇对话系统公开(公告)号：US20120232898A1
公开(公告)日：2012-09-13
申请号：US13476150
申请日：2012-05-21
CPC分类号： G10L15/063 , G10L15/183 , G10L15/22
摘要： The invention relates to a system and method for gathering data for use in a spoken dialog system. An aspect of the invention is generally referred to as an automated hidden human that performs data collection automatically at the beginning of a conversation with a user in a spoken dialog system. The method comprises presenting an initial prompt to a user, recognizing a received user utterance using an automatic speech recognition engine and classifying the recognized user utterance using a spoken language understanding module. If the recognized user utterance is not understood or classifiable to a predetermined acceptance threshold, then the method re-prompts the user. If the recognized user utterance is not classifiable to a predetermined rejection threshold, then the method transfers the user to a human as this may imply a task-specific utterance. The received and classified user utterance is then used for training the spoken dialog system.
摘要翻译： 本发明涉及一种用于收集在口头对话系统中使用的数据的系统和方法。 本发明的一个方面通常被称为在与对话系统中的用户的对话开始时自动执行数据收集的自动隐藏人。 该方法包括向用户呈现初始提示，使用自动语音识别引擎识别接收到的用户话语，并使用口语理解模块对所识别的用户话语进行分类。 如果识别的用户话语不能被理解或可被分类到预定的接受阈值，则该方法重新提示用户。 如果识别的用户话语不能被分类为预定的拒绝阈值，则该方法将用户转移给人，因为这可能意味着任务特定的话语。 然后，接收和分类的用户话语用于训练口语对话系统。
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公开(公告)号：US20090254344A1
公开(公告)日：2009-10-08
申请号：US12485103
申请日：2009-06-16
申请人： Dilek Z. Hakkani-Tur , Mazin G. Rahim , Gokhan Tur
发明人： Dilek Z. Hakkani-Tur , Mazin G. Rahim , Gokhan Tur
IPC分类号： G10L15/04
CPC分类号： G10L15/1822
摘要： A spoken language understanding method and system are provided. The method includes classifying a set of labeled candidate utterances based on a previously trained classifier, generating classification types for each candidate utterance, receiving confidence scores for the classification types from the trained classifier, sorting the classified utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, and rechecking candidate utterances according to the analysis. The system includes modules configured to control a processor in the system to perform the steps of the method.
摘要翻译： 提供口语理解方法和系统。 该方法包括基于先前训练的分类器对一组标记的候选话语进行分类，为每个候选语音生成分类类型，从训练分类器接收分类类型的置信度分数， 每个候选话语与候选话语的相应标签相比较，并且根据分析重新检查候选话语。 该系统包括被配置为控制系统中的处理器以执行该方法的步骤的模块。
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公开(公告)号：US07562017B1
公开(公告)日：2009-07-14
申请号：US11862656
申请日：2007-09-27
申请人： Dilek Z. Hakkani-Tur , Mazin G. Rahim , Gokhan Tur
发明人： Dilek Z. Hakkani-Tur , Mazin G. Rahim , Gokhan Tur
CPC分类号： G10L15/1822
摘要： An active labeling process is provided that aims to minimize the number of utterances to be checked again by automatically selecting the ones that are likely to be erroneous or inconsistent with the previously labeled examples. In one embodiment, the errors and inconsistencies are identified based on the confidences obtained from a previously trained classifier model. In a second embodiment, the errors and inconsistencies are identified based on an unsupervised learning process. In both embodiments, the active labeling process is not dependent upon the particular classifier model.
摘要翻译： 提供了一种主动标注过程，其目的是通过自动选择可能是错误的或与先前标记的示例不一致的那些来最小化要再次检查的话语的数量。 在一个实施例中，基于从先前训练的分类器模型获得的信心来识别误差和不一致性。 在第二实施例中，基于无监督的学习过程来识别错误和不一致。 在两个实施方案中，活性标记过程不依赖于特定的分类器模型。
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