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1.发明授权Keyword detection with international phonetic alphabet by foreground model and background model 有权用前景模型和背景模型对国际语音字母进行关键词检测公开(公告)号：US09466289B2
公开(公告)日：2016-10-11
申请号：US14103775
申请日：2013-12-11
CPC classification number: G10L15/063 , G10L15/08 , G10L2015/088
Abstract: An electronic device with one or more processors and memory trains an acoustic model with an international phonetic alphabet (IPA) phoneme mapping collection and audio samples in different languages, where the acoustic model includes: a foreground model; and a background model. The device generates a phone decoder based on the trained acoustic model. The device collects keyword audio samples, decodes the keyword audio samples with the phone decoder to generate phoneme sequence candidates, and selects a keyword phoneme sequence from the phoneme sequence candidates. After obtaining the keyword phoneme sequence, the device detects one or more keywords in an input audio signal with the trained acoustic model, including: matching phonemic keyword portions of the input audio signal with phonemes in the keyword phoneme sequence with the foreground model; and filtering out phonemic non-keyword portions of the input audio signal with the background model.
Abstract translation: 具有一个或多个处理器和存储器的电子设备具有使用不同语言的国际语音字母（IPA）音素映射收集和音频样本的声学模型，其中声学模型包括：前景模型; 和背景模型。 该设备基于经过训练的声学模型生成电话解码器。 设备收集关键字音频样本，用手机解码器解码关键词音频样本，以产生音素序列候选，并从音素序列候选中选择关键词音素序列。 在获得关键字音素序列之后，设备利用经训练的声学模型检测输入音频信号中的一个或多个关键词，包括：使用前景模型将关键字音素序列中的输入音频信号的音素关键词部分与音素相匹配; 并用背景模型滤出输入音频信号的音素非关键字部分。
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公开(公告)号：US09230541B2
公开(公告)日：2016-01-05
申请号：US14567969
申请日：2014-12-11
Inventor： Lu Ll , Li Lu , Jianxiong Ma , Linghui Kong , Feng Rao , Shuai Yue , Xiang Zhang , Haibo Liu , Eryu Wang , Bo Chen
IPC: G10L15/08
CPC classification number: G10L15/08 , G10L15/083 , G10L2015/088
Abstract: This application discloses a method implemented of recognizing a keyword in a speech that includes a sequence of audio frames further including a current frame and a subsequent frame. A candidate keyword is determined for the current frame using a decoding network that includes keywords and filler words of multiple languages, and used to determine a confidence score for the audio frame sequence. A word option is also determined for the subsequent frame based on the decoding network, and when the candidate keyword and the word option are associated with two distinct types of languages, the confidence score of the audio frame sequence is updated at least based on a penalty factor associated with the two distinct types of languages. The audio frame sequence is then determined to include both the candidate keyword and the word option by evaluating the updated confidence score according to a keyword determination criterion.
Abstract translation: 本申请公开了一种实现的方法，其中识别语音中的关键字，其中包括进一步包括当前帧和后续帧的音频帧序列。 使用包括多种语言的关键词和填充词的解码网络为当前帧确定候选关键字，并且用于确定音频帧序列的置信度分数。 还基于解码网络为后续帧确定字选项，并且当候选关键词和词选项与两种不同类型的语言相关联时，至少基于惩罚来更新音频帧序列的置信度得分 与两种不同类型语言相关联的因素。 然后通过根据关键字确定标准评估更新的可信度得分，确定音频帧序列以包括候选关键词和词选项。
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公开(公告)号：US09811517B2
公开(公告)日：2017-11-07
申请号：US14148579
申请日：2014-01-06
Inventor： Haibo Liu , Eryu Wang , Xiang Zhang , Li Lu , Shuai Yue , Qiuge Liu , Bo Chen , Jian Liu , Lu Li
CPC classification number: G06F17/273 , G06F17/2775 , G06F17/2785 , G06F17/289 , G10L15/265
Abstract: A method of processing information content based on a Chinese language model is performed at a computer, the method including: identifying a plurality of expressions in the information content extracted from a speech input through speech recognition that is queued to be processed; dividing the expressions into a plurality of characteristic units according to semantic features and predetermined characteristics associated with each characteristic unit, each including a subset of the expressions and the predetermined characteristics at least including a respective integer number of expressions that are included in the characteristic unit; extracting, from the Chinese language model, a plurality of probabilities for punctuation marks associated with each characteristic unit; and in accordance with the probabilities, associating a respective punctuation mark with each characteristic unit included in the information content. The method further comprises adding punctuation marks based on a weight determined for each punctuation mark.
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公开(公告)号：US09754581B2
公开(公告)日：2017-09-05
申请号：US13903593
申请日：2013-05-28
Inventor： Li Lu , Feng Rao , Song Liu , Zongyao Tang , Xiang Zhang , Shuai Yue , Bo Chen
CPC classification number: G10L15/08 , G06Q10/1097 , G10L15/26 , G10L2015/088
Abstract: The present invention, pertaining to the field of speech recognition, discloses a reminder setting method and apparatus. The method includes: acquiring speech signals; acquiring time information in speech signals by using keyword recognition, and determining reminder time for reminder setting according to the time information; acquiring text sequence corresponding to the speech signals by using continuous speech recognition, and determining reminder content for reminder setting according to the time information and the text sequence; and setting a reminder according to the reminder time and the reminder content. According to the present invention, acquiring time information in speech signals by using keyword recognition ensures correctness of time information extraction, and achieves an effect that correct time information is still acquired by keyword recognition to set a reminder even in the case that a recognized text sequence is incorrect due to poor precision in whole text recognition in the speech recognition.
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公开(公告)号：US09697821B2
公开(公告)日：2017-07-04
申请号：US14108223
申请日：2013-12-16
Inventor： Feng Rao , Li Lu , Bo Chen , Shuai Yue , Xiang Zhang , Eryu Wang , Dadong Xie , Lou Li , Duling Lu
IPC: G10L15/06 , G10L15/183 , G10L15/197 , G10L15/26
CPC classification number: G10L15/063 , G10L15/183 , G10L15/197 , G10L15/26
Abstract: An automatic speech recognition method includes at a computer having one or more processors and memory for storing one or more programs to be executed by the processors, obtaining a plurality of speech corpus categories through classifying and calculating raw speech corpus; obtaining a plurality of classified language models that respectively correspond to the plurality of speech corpus categories through a language model training applied on each speech corpus category; obtaining an interpolation language model through implementing a weighted interpolation on each classified language model and merging the interpolated plurality of classified language models; constructing a decoding resource in accordance with an acoustic model and the interpolation language model; and decoding input speech using the decoding resource, and outputting a character string with a highest probability as a recognition result of the input speech.
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Patent Agency Ranking
公开(公告)号：US20160086609A1
公开(公告)日：2016-03-24
申请号：US14958606
申请日：2015-12-03
Inventor： Shuai Yue , Xiang Zhang , Li Lu , Feng Rao , Eryu Wang , Haibo Liu , Bo Chen , Jian Liu , Lu Li
CPC classification number: G10L17/24 , G06F3/167 , G10L15/22 , G10L17/02 , G10L17/16 , G10L17/26 , G10L2015/223
Abstract: The present application discloses a method, an electronic system and a non-transitory computer readable storage medium for recognizing audio commands in an electronic device. The electronic device obtains audio data based on an audio signal provided by a user and extracts characteristic audio fingerprint features from the audio data. The electronic device further determines whether the corresponding audio signal is generated by an authorized user by comparing the characteristic audio fingerprint features with an audio fingerprint model for the authorized user and with a universal background model that represents user-independent audio fingerprint features, respectively. When the corresponding audio signal is generated by the authorized user of the electronic device, an audio command is extracted from the audio data, and an operation is performed according to the audio command.
Abstract translation: 本申请公开了一种用于识别电子设备中的音频命令的方法，电子系统和非暂时性计算机可读存储介质。 电子设备基于由用户提供的音频信号获得音频数据，并从音频数据中提取特征音频指纹特征。 电子设备还通过将特征音频指纹特征与用于授权用户的音频指纹模型进行比较，以及分别表示用户独立的音频指纹特征的通用背景模型来确定对应的音频信号是否由授权用户产生。 当由电子设备的授权用户产生相应的音频信号时，从音频数据中提取音频命令，并根据音频命令进行操作。
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7.发明授权公开(公告)号：US09177131B2
公开(公告)日：2015-11-03
申请号：US14262665
申请日：2014-04-25
Inventor： Xiang Zhang , Li Lu , Eryu Wang , Shuai Yue , Feng Rao , Haibo Liu , Lou Li , Duling Lu , Bo Chen
CPC classification number: G06F21/32 , G06F2221/2117
Abstract: A computer-implemented method is performed at a server having one or more processors and memory storing programs executed by the one or more processors for authenticating a user from video and audio data. The method includes: receiving a login request from a mobile device, the login request including video data and audio data; extracting a group of facial features from the video data; extracting a group of audio features from the audio data and recognizing a sequence of words in the audio data; identifying a first user account whose respective facial features match the group of facial features and a second user account whose respective audio features match the group of audio features. If the first user account is the same as the second user account, retrieve the sequence of words associated with the user account and compare the sequences of words for authentication purpose.
Abstract translation: 在具有一个或多个处理器的服务器和由一个或多个处理器执行的用于从视频和音频数据认证用户的存储器存储程序的服务器执行计算机实现的方法。 该方法包括：从移动设备接收登录请求，登录请求包括视频数据和音频数据; 从视频数据中提取一组面部特征; 从音频数据提取一组音频特征并识别音频数据中的单词序列; 识别其各自的面部特征与该组面部特征相匹配的第一用户帐户和其各个音频特征与该组音频特征相匹配的第二用户帐户。 如果第一个用户帐户与第二个用户帐户相同，则检索与用户帐户相关联的单词序列，并比较用于验证目的的单词序列。
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公开(公告)号：US10013985B2
公开(公告)日：2018-07-03
申请号：US14958606
申请日：2015-12-03
Inventor： Shuai Yue , Xiang Zhang , Li Lu , Feng Rao , Eryu Wang , Haibo Liu , Bo Chen , Jian Liu , Lu Li
CPC classification number: G10L17/24 , G06F3/167 , G10L15/22 , G10L17/02 , G10L17/16 , G10L17/26 , G10L2015/223
Abstract: The present application discloses a method, an electronic system and a non-transitory computer readable storage medium for recognizing audio commands in an electronic device. The electronic device obtains audio data based on an audio signal provided by a user and extracts characteristic audio fingerprint features from the audio data. The electronic device further determines whether the corresponding audio signal is generated by an authorized user by comparing the characteristic audio fingerprint features with an audio fingerprint model for the authorized user and with a universal background model that represents user-independent audio fingerprint features, respectively. When the corresponding audio signal is generated by the authorized user of the electronic device, an audio command is extracted from the audio data, and an operation is performed according to the audio command.
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公开(公告)号：US09558741B2
公开(公告)日：2017-01-31
申请号：US14291138
申请日：2014-05-30
Inventor： Lou Li , Li Lu , Xiang Zhang , Feng Rao , Shuai Yue , Bo Chen , Jianxiong Ma , Haibo Liu
IPC: G10L15/28 , G10L15/08 , G10L15/18 , G10L15/183
CPC classification number: G10L15/083 , G10L15/1815 , G10L15/183
Abstract: Systems and methods are provided for speech recognition. For example, audio characteristics are extracted from acquired voice signals; a syllable confusion network is identified based on at least information associated with the audio characteristics; a word lattice is generated based on at least information associated with the syllable confusion network and a predetermined phonetic dictionary; and an optimal character sequence is calculated in the word lattice as a speech recognition result.
Abstract translation: 提供了语音识别的系统和方法。 例如，从获取的语音信号中提取音频特性; 至少基于与音频特征相关联的信息来识别音节混淆网络; 基于至少与音节混淆网络和预定语音字典相关联的信息生成单词格点; 并且在单词格中计算出最佳字符序列作为语音识别结果。
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公开(公告)号：US09508347B2
公开(公告)日：2016-11-29
申请号：US14108237
申请日：2013-12-16
CPC classification number: G10L15/34 , G06N3/02 , G10L15/063 , G10L15/16
Abstract: A method and a device for training a DNN model includes: at a device including one or more processors and memory: establishing an initial DNN model; dividing a training data corpus into a plurality of disjoint data subsets; for each of the plurality of disjoint data subsets, providing the data subset to a respective training processing unit of a plurality of training processing units operating in parallel, wherein the respective training processing unit applies a Stochastic Gradient Descent (SGD) process to update the initial DNN model to generate a respective DNN sub-model based on the data subset; and merging the respective DNN sub-models generated by the plurality of training processing units to obtain an intermediate DNN model, wherein the intermediate DNN model is established as either the initial DNN model for a next training iteration or a final DNN model in accordance with a preset convergence condition.
Abstract translation: 用于训练DNN模型的方法和设备包括：在包括一个或多个处理器和存储器的设备上：建立初始DNN模型; 将训练数据语料库划分为多个不相交的数据子集; 对于多个不相交数据子集中的每一个，将数据子集提供给并行操作的多个训练处理单元的相应训练处理单元，其中各训练处理单元应用随机梯度下降（SGD）过程来更新初始 DNN模型基于数据子集生成相应的DNN子模型; 并且合并由多个训练处理单元生成的各个DNN子模型，以获得中间DNN模型，其中中间DNN模型被建立为用于下一个训练迭代的初始DNN模型或根据下面的训练迭代的最终DNN模型 预设收敛条件。
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