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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：US20190102373A1
公开(公告)日：2019-04-04
申请号：US16133440
申请日：2018-09-17
Inventor： Lou Li , Qiang Cheng , Feng Rao , Li Lu , Xiang Zhang , Shuai Yue , Bo Chen , Duling Lu
IPC: G06F17/27
Abstract: A method is performed at a computer for automatically correcting typographical errors. The computer selects a target word in a target sentence and identifies a target word therein as having a typographical error and first and second sequences of words separated by the target word as context. After identifying, among a database of grammatically correct sentences, a set of sentences having the first and second sequences of words, each sentence including a replacement word, the computer selects a set of candidate grammatically correct sentences whose corresponding replacement words have similarities to the target word above a pre-set threshold, Finally, the computer chooses, among the set of candidate grammatically correct sentences, a fittest grammatically correct sentence according to a linguistic model and replaces the target word in the target sentence with the replacement word within the fittest grammatically correct sentence.
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4.发明授权公开(公告)号：US09805715B2
公开(公告)日：2017-10-31
申请号：US14106634
申请日：2013-12-13
Inventor： Shuai Yue , Li Lu , Xiang Zhang , Dadong Xie , Haibo Liu , Bo Chen , Jian Liu
CPC classification number: G10L15/14 , G10L15/063 , G10L15/083 , G10L15/32 , G10L2015/088 , G10L2015/223
Abstract: A method of recognizing speech commands includes generating a background acoustic model for a sound using a first sound sample, the background acoustic model characterized by a first precision metric. A foreground acoustic model is generated for the sound using a second sound sample, the foreground acoustic model characterized by a second precision metric. A third sound sample is received and decoded by assigning a weight to the third sound sample corresponding to a probability that the sound sample originated in a foreground using the foreground acoustic model and the background acoustic model. The method further includes determining if the weight meets predefined criteria for assigning the third sound sample to the foreground and, when the weight meets the predefined criteria, interpreting the third sound sample as a portion of a speech command. Otherwise, recognition of the third sound sample as a portion of a speech command is forgone.
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公开(公告)号：US09502038B2
公开(公告)日：2016-11-22
申请号：US14105110
申请日：2013-12-12
Inventor： Eryu Wang , Li Lu , Xiang Zhang , Haibo Liu , Lou Li , Feng Rao , Duling Lu , Shuai Yue , Bo Chen
Abstract: A method and device for voiceprint recognition, include: establishing a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data; obtaining a plurality of high-level voiceprint features by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, and the tuning producing a second-level DNN model specifying the plurality of high-level voiceprint features; based on the second-level DNN model, registering a respective high-level voiceprint feature sequence for a user based on a registration speech sample received from the user; and performing speaker verification for the user based on the respective high-level voiceprint feature sequence registered for the user.
Abstract translation: 用于声纹识别的方法和装置包括：基于未标记的语音数据建立第一级深神经网络（DNN）模型，不包含扬声器标签的未标记语音数据和指定多个基本声纹特征的第一级DNN模型 对于未标记的语音数据; 通过基于标记的语音数据调整第一级DNN模型来获得多个高级声纹特征，所述标记语音数据包含具有相应扬声器标签的语音样本，并且调谐产生指定多个高的DNN模型 级的声纹特征; 基于第二级DNN模型，基于从用户接收到的注册语音样本，为用户注册相应的高级声纹特征序列; 以及基于为用户注册的各个高级声纹特征序列，为用户执行说话人验证。
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Patent Agency Ranking
公开(公告)号：US09396723B2
公开(公告)日：2016-07-19
申请号：US14109845
申请日：2013-12-17
IPC: G10L15/00 , G10L15/06 , G06F17/28 , G10L15/183
CPC classification number: G10L15/063 , G06F17/28 , G10L15/183
Abstract: A method and a device for training an acoustic language model, include: conducting word segmentation for training samples in a training corpus using an initial language model containing no word class labels, to obtain initial word segmentation data containing no word class labels; performing word class replacement for the initial word segmentation data containing no word class labels, to obtain first word segmentation data containing word class labels; using the first word segmentation data containing word class labels to train a first language model containing word class labels; using the first language model containing word class labels to conduct word segmentation for the training samples in the training corpus, to obtain second word segmentation data containing word class labels; and in accordance with the second word segmentation data meeting one or more predetermined criteria, using the second word segmentation data containing word class labels to train the acoustic language model.
Abstract translation: 一种用于训练声学语言模型的方法和装置，包括：使用不含词类标签的初始语言模型，在训练语料库中训练样本的词分割，以获得不包含词类标签的初始分词数据; 对不包含词类标签的初始分词数据执行单词类替换，以获得包含单词分类标签的第一分词数据; 使用包含词类标签的第一词分割数据来训练包含词类标签的第一语言模型; 使用包含词类标签的第一语言模型对训练语料库中的训练样本进行词分割，以获得包含词类标签的第二词分割数据; 并且根据满足一个或多个预定标准的第二字分割数据，使用包含词类标签的第二词分割数据来训练声学语言模型。
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公开(公告)号：US20150095032A1
公开(公告)日：2015-04-02
申请号：US14567969
申请日：2014-12-11
Inventor： Lu LI , 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：US09940935B2
公开(公告)日：2018-04-10
申请号：US15240696
申请日：2016-08-18
Inventor： Eryu Wang , Li Lu , Xiang Zhang , Haibo Liu , Lou Li , Feng Rao , Duling Lu , Shuai Yue , Bo Chen
Abstract: A method is performed at a device having one or more processors and memory. The device establishes a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data. The device establishes a second-level DNN model by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, wherein the second-level DNN model specifies a plurality of high-level voiceprint features. Using the second-level DNN model, registers a first high-level voiceprint feature sequence for a user based on a registration speech sample received from the user. The device performs speaker verification for the user based on the first high-level voiceprint feature sequence registered for the user.
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公开(公告)号：US09779728B2
公开(公告)日：2017-10-03
申请号：US14160808
申请日：2014-01-22
IPC: G10L15/00 , G10L15/04 , G10L15/18 , G10L15/26 , G10L15/187 , G06F17/27 , G10L15/183
CPC classification number: G10L15/1815 , G06F17/27 , G06F17/2725 , G10L15/04 , G10L15/183 , G10L15/187 , G10L15/26 , G10L15/265
Abstract: Systems and methods are provided for adding punctuations. For example, one or more first feature units are identified in a voice file taken as a whole; the voice file is divided into multiple segments by detecting silences in the voice file; one or more second feature units are identified in the voice file; a first aggregate weight of first punctuation states of the voice file and a second aggregate weight of second punctuation states of the voice file are determined, using a language model established based on word separation and third semantic features; a weighted calculation is performed to generate a third aggregate weight based on a linear combination associated with the first aggregate weight and the second aggregate weight; and one or more final punctuations are added to the voice file based on at least information associated with the third aggregate weight.
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公开(公告)号：US09355637B2
公开(公告)日：2016-05-31
申请号：US14620000
申请日：2015-02-11
Inventor： Jianxiong Ma , Lu Li , Li Lu , Xiang Zhang , Shuai Yue , Feng Rao , Eryu Wang , Linghui Kong
CPC classification number: G10L15/18 , G10L15/08 , G10L15/28 , G10L15/32 , G10L2015/088
Abstract: A method and an apparatus are provided for retrieving keyword. The apparatus configures at least two types of language models in a model file, where each type of language model includes a recognition model and a corresponding decoding model; the apparatus extracts a speech feature from the to-be-processed speech data; performs language matching on the extracted speech feature by using recognition models in the model file one by one, and determines a recognition model based on a language matching rate; and determines a decoding model corresponding to the recognition model; decoding the extracted speech feature by using the determined decoding model, and obtains a word recognition result after the decoding; and matches a keyword in a keyword dictionary and the word recognition result, and outputs a matched keyword.
Abstract translation: 提供了一种用于检索关键字的方法和装置。 该装置在模型文件中配置至少两种类型的语言模型，其中每种类型的语言模型包括识别模型和相应的解码模型; 该设备从待处理语音数据中提取语音特征; 通过在模型文件中逐一使用识别模型对提取出的语音特征进行语言匹配，并根据语言匹配率确定识别模型; 并确定与识别模型相对应的解码模型; 通过使用所确定的解码模型来解码所提取的语音特征，并且在解码之后获得字识别结果; 并且将关键词字典中的关键字与单词识别结果进行匹配，并输出匹配的关键字。
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