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1. 发明授权

US11842747B2 Calculating numbers of clusters in data sets using eigen response analysis 有权
公开(公告)号：US11842747B2
公开(公告)日：2023-12-12
申请号：US17508428
申请日：2021-10-22
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz
IPC分类号： G10L25/18 , G10L15/16 , G06V10/75 , G06F18/232
CPC分类号： G10L25/18 , G06F18/232 , G06V10/76 , G10L15/16
摘要： An example system includes a processor to receive a data set and similarity scores. The processor is to execute an eigen response analysis on eigenvectors calculated for a similarity matrix generated based on the similarity scores for the data set. The processor is to output an estimated number of clusters in the data set based on the eigen response analysis.

2. 发明授权

US09837080B2 Detection of target and non-target users using multi-session information 有权
公开(公告)号：US09837080B2
公开(公告)日：2017-12-05
申请号：US14465415
申请日：2014-08-21
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz , Shay Ben-David , David Nahamoo , Jason W. Pelecanos , Orith Toledo-Ronen
IPC分类号： G10L17/06
CPC分类号： G10L17/06
摘要： Systems and methods for maintaining speaker recognition performance are provided. A method for maintaining speaker recognition performance, comprises training a plurality of models respectively corresponding to speaker recognition scores from a plurality of speakers over a plurality of sessions, and using the plurality of models to conclude whether a speaker seeking access to an environment is a non-ideal target speaker or a non-ideal non-target speaker. Using the plurality of models to conclude comprises calculating a first probability that the speaker seeking access is the non-ideal target speaker, calculating a second probability that the speaker seeking access is the non-ideal non-target speaker, and determining whether the first probability, the second probability or a sum of the first probability and the second probability is above a probability threshold.

3. 发明申请

US20170069313A1 COVARIANCE MATRIX ESTIMATION WITH STRUCTURAL-BASED PRIORS FOR SPEECH PROCESSING 审中-公开
标题翻译：用于语音处理的基于结构的前提的协变矩阵估计
公开(公告)号：US20170069313A1
公开(公告)日：2017-03-09
申请号：US14846775
申请日：2015-09-06
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz
IPC分类号： G10L15/14 , G10L15/20 , G10L19/02 , G10L25/24
CPC分类号： G10L15/14 , G10L15/20 , G10L17/02 , G10L19/02 , G10L25/24
摘要： According to some embodiments of the present invention there is provided a computerized method for speech processing using a Gaussian Mixture Model. The method comprises the action of receiving by hardware processor(s) two or more covariance values representing relationships between distributions of speech coefficient values that represent two or more audible input speech signals recorded by a microphone. The method comprises the action of computing two or more eigenvectors and eignevalues using a principle component analysis of the covariance values and transforming the speech coefficient values using the eigenvectors and computing two or more second covariance values from the transformed speech coefficient values. The method comprises the action of modifying some of the second covariance values according to the eignevalues, the covariance values, and two or more indices of the speech coefficient values. The second covariance values to the speech processor comprising the Gaussian Mixture Model.
摘要翻译：根据本发明的一些实施例，提供了一种使用高斯混合模型的语音处理的计算机化方法。该方法包括由硬件处理器接收表示由麦克风记录的表示两个或多个可听输入语音信号的语音系数值的分布之间的关系的两个或多个协方差值的动作。该方法包括使用协方差值的主成分分析来计算两个或多个特征向量和特征值的动作，并使用特征向量变换语音系数值，并从变换的语音系数值计算两个或更多个第二协方差值。该方法包括根据语音系数值的特征值，协方差值和两个或多个索引来修改一些第二协方差值的动作。包括高斯混合模型的语音处理器的第二协方差值。

4. 发明公开

US20240185120A1 SELF-SUPERVISED LEARNING OF A TASK WITH NORMALIZATION OF NUISANCE FROM A DIFFERENT TASK 审中-公开
公开(公告)号：US20240185120A1
公开(公告)日：2024-06-06
申请号：US18074536
申请日：2022-12-05
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz , Itai Gat
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A computer-implemented method including: obtaining a pre-trained upstream machine learning model; fine-tuning the pre-trained upstream model for at least two downstream tasks, wherein the fine-tuning comprises: (a) training a target downstream model for a target downstream task, based on a dataset with labeling specific to the target downstream task, and (b) training a nuisance downstream model for a nuisance downstream task, based on a dataset with labeling of characteristics which are specific to the nuisance downstream task and are undesired for the target downstream task; and normalizing the undesired characteristics from the pre-trained upstream model, to prevent biasing of the target downstream model by the undesired characteristics.

5. 发明授权

US11211053B2 Systems and methods for automated generation of subtitles 有权
公开(公告)号：US11211053B2
公开(公告)日：2021-12-28
申请号：US16420207
申请日：2019-05-23
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz , Zvi Kons
IPC分类号： G10L15/07 , H04N19/186 , G10L15/26
摘要： There is provided a computer implemented method of presenting color coded text generated from an audio track of a video, the color coding denoting respective speakers, comprising: receiving the audio track of the video divided into a plurality of audio-segments each representing speech spoken by a respective speaker of a plurality of speakers, for each audio-segment of the plurality of audio-segments: receiving a text representation of the audio-segment, extracting a feature vector from the audio-segment, mapping the feature vector to a color space, coloring the text representation according to the color space, and presenting the colored text representation in association with a video-segment corresponding to the audio-segment.

6. 发明申请

US20180034859A1 Dynamic Multi-Factor Authentication Challenge Generation 审中-公开
公开(公告)号：US20180034859A1
公开(公告)日：2018-02-01
申请号：US15221620
申请日：2016-07-28
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz , Lawrence Koved , Ian M. Molloy , Bo Zhang
IPC分类号： H04L29/06
CPC分类号： H04L63/205 , H04L63/08 , H04L63/0861 , H04L63/1466 , H04L2463/082
摘要： Dynamic multi-factor authentication challenge selection is provided. A risk associated with an operation that requires authentication of a user of a client device is determined. A plurality of authentication methods is identified. Each respective authentication method associated with a level of security offsetting the risk and a computing cost associated with a respective authentication method. One or more authentication methods are selected from the plurality of authentication methods according to the risk and to minimize the computing cost associated with authenticating the operation.

7. 发明授权

US09405893B2 Biometric authentication 有权
标题翻译：生物识别认证
公开(公告)号：US09405893B2
公开(公告)日：2016-08-02
申请号：US14172928
申请日：2014-02-05
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz , Amir Geva , Ron Hoory , David Nahamoo , Jason William Pelecanos , Orith Toledo-Ronen
IPC分类号： G06F21/32 , G06K9/00 , G06N99/00 , G06K9/62
CPC分类号： G06F21/32 , G06K9/00892 , G06K9/629 , G06N99/005
摘要： A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly, wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the at least first and second enrollment supervectors to a single enrollment generic supervector and the at least first and second verification supervectors to a single verification generic supervector; and verifying an identity of the target subject based on a fused score calculated for the verification generic supervector, wherein the fused score is calculated based on the enrollment generic supervector and the set of development generic supervectors.
摘要翻译：一种方法，包括使用至少一个硬件处理器：提供表示多个对象的生物特征样本的特征的一组开发超级生物，所述生物特征样本是至少第一和第二不同的生物特征模态; 提供至少第一和第二注册超级代理，其对应地代表目标对象的至少第一和第二注册生物特征样本的特征，其中所述至少第一和第二注册样本是至少第一和第二不同生物特征模态相应地提供至少第一和第二验证超级向量，其相应地代表目标对象的至少第一和第二验证生物测定样本的特征，其中所述至少第一和第二验证样本相应地具有至少第一和第二不同生物特征模态 ; 将开发超级用户连接到一组开发通用超向量，至少第一和第二注册超级用户单个注册通用超向量，以及至少第一和第二验证超级用户到单个验证通用超向量; 以及基于针对所述验证通用超向量计算的融合分数来验证所述目标对象的身份，其中，基于所述注册通用超向量和所述开发通用超级向量集合来计算所述融合分数。

8. 发明授权

US10628567B2 User authentication using prompted text 有权
公开(公告)号：US10628567B2
公开(公告)日：2020-04-21
申请号：US15256632
申请日：2016-09-05
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz
IPC分类号： G06F21/32 , H04L29/06 , G10L17/04 , G10L17/06 , G10L17/24
CPC分类号： G06F21/32 , G10L17/04 , G10L17/06 , G10L17/24 , H04L63/0861 , H04L63/123
摘要： Methods, computing systems and computer program products implement embodiments of the present invention that include defining a verification string including a sequence of verification characters and a delimiter character between each sequential pair of the verification characters, the delimiter character being different from the verification characters. The verification string to a user, and upon receiving, from the user, a series of verification vocal inputs in response to presenting the verification string, a set of verification features from each of the verification vocal inputs are computed so as to generate sets of verification features. A one-to-one correspondence is established between each of the verification vocal inputs and each of the verification characters, and the user is authenticated based on the verification vocal inputs and their corresponding sets of verification features.

9. 发明授权

US10509895B2 Biometric authentication 有权
公开(公告)号：US10509895B2
公开(公告)日：2019-12-17
申请号：US15064632
申请日：2016-03-09
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz , Amir Geva , Ron Hoory , David Nahamoo , Jason William Pelecanos , Orith Toledo-Ronen
IPC分类号： G06F21/32 , G06K9/00 , G06N20/00 , G06K9/62
摘要： A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly, wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the at least first and second enrollment supervectors to a single enrollment generic supervector and the at least first and second verification supervectors to a single verification generic supervector; and verifying an identity of the target subject based on a fused score calculated for the verification generic supervector, wherein the fused score is calculated based on the enrollment generic supervector and the set of development generic supervectors.

10. 发明授权

US10936708B2 Biometric data protection 有权
公开(公告)号：US10936708B2
公开(公告)日：2021-03-02
申请号：US16148088
申请日：2018-10-01
申请人： International Business Machines Corporation
发明人： Hagai Aronowitz
IPC分类号： G06F21/32 , G06F11/10 , G06F16/22
摘要： Aspects of the present disclosure relate to enhancing biometric security. A biometric template and a biometric sample can be compared using a first security technique having a first ECC framework with a first number of codewords. A determination can be made that the biometric template and biometric sample do not match using the first security technique. The biometric template and the biometric sample can then be compared using a second security technique having a second ECC framework with a second number of codewords, the second number of codewords less than the first number of codewords. A determination can then be made that the biometric template and the biometric sample match, and a user associated with the biometric sample can be verified.

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