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    • 31. 发明授权
    • Combined speech recognition and sound recording
    • 组合语音识别和录音
    • US07505911B2
    • 2009-03-17
    • US11005568
    • 2004-12-05
    • Daniel L. RothJordan R. CohenDavid F. JohnstonEdward W. Porter
    • Daniel L. RothJordan R. CohenDavid F. JohnstonEdward W. Porter
    • G01L21/06
    • G10L15/22G10L15/26G10L2015/225
    • A handheld device with both large-vocabulary speech recognition and audio recoding allows users to switch between at least two of the following three modes: (1) recording audio without corresponding speech recognition; (2) recording with speech recognition; and (3) speech recognition without audio recording. A handheld device with both large-vocabulary speech recognition and audio recoding enables a user to select a portion of previously recorded sound and have speech recognition performed upon it. A system enables a user to search for a text label associated with portions of unrecognized recorded sound by uttering the label's words. A large-vocabulary system allows users to switch between playing back recorded audio and speech recognition with a single input, with successive audio playbacks automatically starting slightly before the end of prior playback. And a cell phone that allows both large-vocabulary speech recognition and audio recording and playback.
    • 具有大词汇语音识别和音频重新编码的手持设备允许用户在以下三种模式中的至少两种之间进行切换:(1)记录没有相应语音识别的音频; (2)用语音识别录音; 和(3)没有录音的语音识别。 具有大词汇语音识别和音频重新编码的手持设备使得用户能够选择先前记录的声音的一部分并且对其进行语音识别。 系统使用户能够通过发出标签的单词来搜索与未被识别的记录声音的部分相关联的文本标签。 大词汇系统允许用户使用单个输入在回放记录的音频和语音识别之间切换,连续的音频播放在先前播放结束之前自动开始。 和一个手机,允许大词汇语音识别和音频录音和播放。
    • 33. 发明授权
    • Speech recognition using selectable recognition modes
    • 使用可选识别模式进行语音识别
    • US07313526B2
    • 2007-12-25
    • US10950092
    • 2004-09-24
    • Daniel L. RothJordan R. CohenDavid F. JohnstonManfred G. Grabherr
    • Daniel L. RothJordan R. CohenDavid F. JohnstonManfred G. Grabherr
    • G10L11/00G10L15/28G10L15/04G06F3/00
    • G10L15/22G10L15/19
    • The present invention relates to speech recognition using selectable recognition modes. This includes innovations such as: large vocabulary speech recognition programming that supplies recognized words to external program as they are recognized, and allows a user to select between large vocabulary recognition of an utterance with and without language context from the prior utterance independently of state of the external program; allowing a user to select between continuous and discrete speech recognition that use substantially the same vocabulary; allowing a user to select between continuous and discrete large-vocabulary speech recognition modes; allowing a user to select between at least two different alphabetic entry speech recognition modes; and allowing a user to select from among four or more of the following recognitions modes when creating text: a large-vocabulary mode, an alphabetic entry mode, a number entry mode, and a punctuation entry mode.
    • 本发明涉及使用可选择识别模式的语音识别。 这包括创新,例如:大量词汇语音识别程序,在识别出外部程序时,将识别的词提供给外部程序,并允许用户在与先前的语言无关的语言语境的大量词汇识别与非语言语境之间进行选择 外部程序; 允许用户在使用基本相同词汇的连续和离散语音识别之间进行选择; 允许用户在连续和离散的大词汇语音识别模式之间进行选择; 允许用户在至少两个不同的字母进入语音识别模式之间进行选择; 并且允许用户在创建文本时从四种或更多种以下识别模式中进行选择:大词汇模式,字母输入模式,数字输入模式和标点输入模式。
    • 35. 发明授权
    • Training speech recognition word models from word samples synthesized by Monte Carlo techniques
    • 通过蒙特卡罗技术合成的单词样本训练语音识别词模型
    • US07133827B1
    • 2006-11-07
    • US10361154
    • 2003-02-06
    • Laurence S. GillickDonald R. McAllasterDaniel L. Roth
    • Laurence S. GillickDonald R. McAllasterDaniel L. Roth
    • G10L15/06
    • G10L15/063
    • A new word model is trained from synthetic word samples derived by Monte Carlo techniques from one or more prior word models. The prior word model can be a phonetic word model and the new word model can be a non-phonetic, whole-word, word model. The prior word model can be trained from data that has undergone a first channel normalization and the synthesized word samples from which the new word model is trained can undergo a different channel normalization similar to that to be used in a given speech recognition context. The prior word model can have a first model structure and the new word model can have a second, different, model structure. These differences in model structure can include, for example, differences of model topology; differences of model complexity; and differences in the type of basis function used in a description of such probability distributions.
    • 从一个或多个先前的单词模型通过蒙特卡罗技术衍生的合成词样本训练新的单词模型。 先验词模型可以是语音模型,新词模型可以是非语音,全字,单词模型。 可以从已经经历第一信道规范化的数据训练现有单词模型,并且从其中训练新单词模型的合成单词样本可以经历与在给定语音识别上下文中使用相似的不同信道规范化。 先验词模型可以具有第一模型结构,并且新词模型可以具有第二,不同的模型结构。 模型结构的这些差异可以包括例如模型拓扑的差异; 模型复杂性差异; 以及在这种概率分布的描述中使用的基函数的类型的差异。