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    • 4. 发明申请
    • Voice tagging, voice annotation, and speech recognition for portable devices with optional post processing
    • 语音标记,语音注释和可选后置处理的便携式设备的语音识别
    • US20050075881A1
    • 2005-04-07
    • US10677174
    • 2003-10-02
    • Luca RigazioRobert BomanPatrick NguyenJean-Claude Junqua
    • Luca RigazioRobert BomanPatrick NguyenJean-Claude Junqua
    • G10L15/26G10L21/00
    • G06F17/30796G10L15/26
    • A media capture device has an audio input receptive of user speech relating to a media capture activity in close temporal relation to the media capture activity. A plurality of focused speech recognition lexica respectively relating to media capture activities are stored on the device, and a speech recognizer recognizes the user speech based on a selected one of the focused speech recognition lexica. A media tagger tags captured media with generated speech recognition text, and a media annotator annotates the captured media with a sample of the user speech that is suitable for input to a speech recognizer. Tagging and annotating are based on close temporal relation between receipt of the user speech and capture of the captured media. Annotations may be converted to tags during post processing, employed to edit a lexicon using letter-to-sound rules and spelled word input, or matched directly to speech to retrieve captured media.
    • 媒体捕获设备具有接收与媒体捕获活动紧密相关的媒体捕获活动的用户语音的音频输入。 分别与媒体捕获活动相关的多个聚焦语音识别词典被存储在设备上,并且语音识别器基于所选择的一个焦点语音识别词典识别用户语音。 媒体标签器使用生成的语音识别文本来标记捕获的媒体,并且媒体注释器用适合于输入到语音识别器的用户语音的样本来注释所捕获的媒体。 标记和注释是基于用户语音的接收和捕获的媒体的捕获之间的紧密的时间关系。 在后期处理中,注释可以转换为标签,用于使用字母对声音规则和拼写单词输入来编辑词典,或直接与语音匹配以检索所捕获的媒体。
    • 5. 发明授权
    • Voice tagging, voice annotation, and speech recognition for portable devices with optional post processing
    • 语音标记,语音注释和可选后置处理的便携式设备的语音识别
    • US07324943B2
    • 2008-01-29
    • US10677174
    • 2003-10-02
    • Luca RigazioRobert BomanPatrick NguyenJean-Claude Junqua
    • Luca RigazioRobert BomanPatrick NguyenJean-Claude Junqua
    • G10L21/00H04N5/76
    • G06F17/30796G10L15/26
    • A media capture device has an audio input receptive of user speech relating to a media capture activity in close temporal relation to the media capture activity. A plurality of focused speech recognition lexica respectively relating to media capture activities are stored on the device, and a speech recognizer recognizes the user speech based on a selected one of the focused speech recognition lexica. A media tagger tags captured media with generated speech recognition text, and a media annotator annotates the captured media with a sample of the user speech that is suitable for input to a speech recognizer. Tagging and annotating are based on close temporal relation between receipt of the user speech and capture of the captured media. Annotations may be converted to tags during post processing, employed to edit a lexicon using letter-to-sound rules and spelled word input, or matched directly to speech to retrieve captured media.
    • 媒体捕获设备具有接收与媒体捕获活动紧密相关的媒体捕获活动的用户语音的音频输入。 分别与媒体捕获活动相关的多个聚焦语音识别词典被存储在设备上,并且语音识别器基于所选择的一个焦点语音识别词典识别用户语音。 媒体标签器使用生成的语音识别文本来标记捕获的媒体,并且媒体注释器用适合于输入到语音识别器的用户语音的样本来注释所捕获的媒体。 标记和注释是基于用户语音的接收和捕获的媒体的捕获之间的紧密的时间关系。 在后期处理中,注释可以转换为标签,用于使用字母对声音规则和拼写单词输入来编辑词典,或直接与语音匹配以检索所捕获的媒体。
    • 6. 发明申请
    • Speech data mining for call center management
    • 语音数据挖掘用于呼叫中心管理
    • US20050010411A1
    • 2005-01-13
    • US10616006
    • 2003-07-09
    • Luca RigazioPatrick NguyenJean-Claude JunquaRobert Boman
    • Luca RigazioPatrick NguyenJean-Claude JunquaRobert Boman
    • G10L15/26G10L17/00G10L15/00
    • G10L15/26G10L17/00
    • A speech data mining system for use in generating a rich transcription having utility in call center management includes a speech differentiation module differentiating between speech of interacting speakers, and a speech recognition module improving automatic recognition of speech of one speaker based on interaction with another speaker employed as a reference speaker. A transcript generation module generates a rich transcript based on recognized speech of the speakers. Focused, interactive language models improve recognition of a customer on a low quality channel using context extracted from speech of a call center operator on a high quality channel with a speech model adapted to the operator. Mined speech data includes number of interaction turns, customer frustration phrases, operator polity, interruptions, and/or contexts extracted from speech recognition results, such as topics, complaints, solutions, and resolutions. Mined speech data is useful in call center and/or product or service quality management.
    • 用于产生在呼叫中心管理中具有效用的丰富录音的语音数据挖掘系统包括区分交互式扬声器的语音的语音区分模块和改善一个扬声器的语音的自动识别的语音识别模块, 作为参考发言人。 转录本生成模块基于扬声器的识别语音生成丰富的录音。 专注的交互式语言模型通过使用适合于操作员的语音模型,在高质量频道上从呼叫中心运营商的语音提取的上下文,改善对低质量信道上客户的识别。 挖掘的语音数据包括从诸如主题,投诉,解决方案和分辨率的语音识别结果中提取的交互轮廓数量,客户沮丧短语,运营商政治,中断和/或上下文。 挖掘的语音数据在呼叫中心和/或产品或服务质量管理中是有用的。
    • 10. 发明授权
    • Maximum likelihood method for finding an adapted speaker model in eigenvoice space
    • 在本征语音空间中找到适应的说话者模型的最大似然法
    • US06263309B1
    • 2001-07-17
    • US09070054
    • 1998-04-30
    • Patrick NguyenRoland KuhnJean-Claude Junqua
    • Patrick NguyenRoland KuhnJean-Claude Junqua
    • G10L1508
    • G10L15/07
    • A set of speaker dependent models is trained upon a comparatively large number of training speakers, one model per speaker, and model parameters are extracted in a predefined order to construct a set of supervectors, one per speaker. Principle component analysis is then performed on the set of supervectors to generate a set of eigenvectors that define an eigenvoice space. If desired, the number of vectors may be reduced to achieve data compression. Thereafter, a new speaker provides adaptation data from which a supervector is constructed by constraining this supervector to be in the eigenvoice space based on a maximum likelihood estimation. The resulting coefficients in the eigenspace of this new speaker may then be used to construct a new set of model parameters from which an adapted model is constructed for that speaker. Environmental adaptation may be performed by including environmental variations in the training data.
    • 一组扬声器依赖模型训练在相对较多数量的训练扬声器上,每个扬声器一个模型和模型参数以预定义的顺序提取,以构建一组超级矢量,每个扬声器一个。 然后在一组超级矢量上执行原理分量分析,以生成一组定义本征语音空间的特征向量。 如果需要,可以减少向量的数量以实现数据压缩。 此后,新的说话者提供了通过基于最大似然估计将该超向量限制在本征语音空间中来构建超向量的适配数据。 然后,可以使用这个新的说话者的本征空间中得到的系数来构建一组新的模型参数,从该模型参数构建适合于该说话者的适应模型。 可以通过在训练数据中包括环境变化来执行环境适应。