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公开(公告)号：US20230326462A1
公开(公告)日：2023-10-12
申请号：US18329138
申请日：2023-06-05
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Matthew GARLAND
IPC: G10L17/00 , H04M1/27 , G10L17/24 , G10L15/19 , G10L17/08 , G06N7/01 , G10L15/07 , G10L15/26 , G10L17/04
CPC classification number: G10L17/00 , H04M1/271 , G10L17/24 , G10L15/19 , G10L17/08 , G06N7/01 , G10L15/07 , G10L15/26 , G10L17/04 , H04M2203/40
Abstract: Utterances of at least two speakers in a speech signal may be distinguished and the associated speaker identified by use of diarization together with automatic speech recognition of identifying words and phrases commonly in the speech signal. The diarization process clusters turns of the conversation while recognized special form phrases and entity names identify the speakers. A trained probabilistic model deduces which entity name(s) correspond to the clusters.
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公开(公告)号：US20230290357A1
公开(公告)日：2023-09-14
申请号：US18321353
申请日：2023-05-22
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Matthew GARLAND
IPC: G10L17/20 , G10L17/02 , G10L17/04 , G10L17/18 , G10L19/028
CPC classification number: G10L17/20 , G10L17/02 , G10L17/04 , G10L17/18 , G10L19/028
Abstract: A system for generating channel-compensated features of a speech signal includes a channel noise simulator that degrades the speech signal, a feed forward convolutional neural network (CNN) that generates channel-compensated features of the degraded speech signal, and a loss function that computes a difference between the channel-compensated features and handcrafted features for the same raw speech signal. Each loss result may be used to update connection weights of the CNN until a predetermined threshold loss is satisfied, and the CNN may be used as a front-end for a deep neural network (DNN) for speaker recognition/verification. The DNN may include convolutional layers, a bottleneck features layer, multiple fully-connected layers, and an output layer. The bottleneck features may be used to update connection weights of the convolutional layers, and dropout may be applied to the convolutional layers.
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公开(公告)号：US11727942B2
公开(公告)日：2023-08-15
申请号：US17746832
申请日：2022-05-17
Applicant: Pindrop Security, Inc.
Inventor： Elie Khoury , Matthew Garland
IPC: G10L17/26 , H04L9/40 , G06F21/32 , G10L25/30 , G10L17/18 , G10L17/04 , G10L15/26 , G06V40/10 , G06V40/16 , G06F18/24 , G06V10/764 , G06V10/82 , G06V40/50
CPC classification number: G10L17/26 , G06F18/24 , G06F21/32 , G06V10/764 , G06V10/82 , G06V40/10 , G06V40/16 , G10L15/26 , G10L17/04 , G10L17/18 , G10L25/30 , H04L63/0861 , G06V40/178 , G06V40/50
Abstract: Systems and methods may generate, by a computer, a voice model for an enrollee based upon a set of one or more features extracted from a first audio sample received at a first time; receive at a second time a second audio sample associated with a caller; generate a likelihood score for the second audio sample by applying the voice model associated with the enrollee on the set of features extracted from the second audio sample associated with the caller, the likelihood score indicating a likelihood that the caller is the enrollee; calibrate the likelihood score based upon a time interval from the first time to the second time and at least one of: an enrollee age at the first time and an enrollee gender; and authenticate the caller as the enrollee upon the computer determining that the likelihood score satisfies a predetermined threshold score.
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公开(公告)号：US20230137652A1
公开(公告)日：2023-05-04
申请号：US17977521
申请日：2022-10-31
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Tianxiang CHEN , Avrosh KUMAR , Ganesh SIVARAMAN , Kedar PHATAK
Abstract: Disclosed are systems and methods including computing-processes executing machine-learning architectures for voice biometrics, in which the machine-learning architecture implements one or more language compensation functions. Embodiments include an embedding extraction engine (sometimes referred to as an “embedding extractor”) that extracts speaker embeddings and determines a speaker similarity score for determine or verifying the likelihood that speakers in different audio signals are the same speaker. The machine-learning architecture further includes a multi-class language classifier that determines a language likelihood score that indicates the likelihood that a particular audio signal includes a spoken language. The features and functions of the machine-learning architecture described herein may implement the various language compensation techniques to provide more accurate speaker recognition results, regardless of the language spoken by the speaker.
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公开(公告)号：US20230037232A1
公开(公告)日：2023-02-02
申请号：US17963091
申请日：2022-10-10
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Matthew GARLAND
Abstract: The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.
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Patent Agency Ranking
公开(公告)号：US20230014180A1
公开(公告)日：2023-01-19
申请号：US17948991
申请日：2022-09-20
Applicant: Pindrop Security, Inc.
Inventor： John Cornwell , Terry Nelms, II
Abstract: Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).
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公开(公告)号：US20220392453A1
公开(公告)日：2022-12-08
申请号：US17832404
申请日：2022-06-03
Applicant: Pindrop Security, Inc.
Inventor： Payas Gupta , Elie KHOURY , Terry Nelms, II , Vijay BALASUBRAMANIYAN
Abstract: Disclosed are systems and methods including computing-processes executing machine-learning architectures extract vectors representing disparate types of data and output predicted identities of users accessing computing services, without express identity assertions, and across multiple computing services, analyzing data from multiple modalities, for various user devices, and agnostic to architectures hosting the disparate computing service. The system invokes the identification operations of the machine-learning architecture, which extracts biometric embeddings from biometric data and context embeddings representing all or most of the types of metadata features analyzed by the system. The context embeddings help identify a subset of potentially matching identities of possible users, which limits the number of biometric-prints the system compares against an inbound biometric embedding for authentication. The types of extracted features originate from multiple modalities, including metadata from data communications, audio signals, and images. In this way, the embodiments apply a multi-modality machine-learning architecture.
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公开(公告)号：US11468901B2
公开(公告)日：2022-10-11
申请号：US16536293
申请日：2019-08-08
Applicant: PINDROP SECURITY, INC.
Inventor： Elie Khoury , Matthew Garland
Abstract: The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.
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公开(公告)号：US20220301554A1
公开(公告)日：2022-09-22
申请号：US17833674
申请日：2022-06-06
Applicant: Pindrop Security, Inc.
Inventor： Hrishikesh Rao
IPC: G10L15/197 , G10L15/04 , G10L15/30 , G10L15/22
Abstract: Embodiments described herein provide for a computer that detects one or more keywords of interest using acoustic features, to detect or query commonalities across multiple fraud calls. Embodiments described herein may implement unsupervised keyword spotting (UKWS) or unsupervised word discovery (UWD) in order to identify commonalities across a set of calls, where both UKWS and UWD employ Gaussian Mixture Models (GMM) and one or more dynamic time-warping algorithms. A user may indicate a training exemplar or occurrence of call-specific information, referred to herein as “a named entity,” such as a person's name, an account number, account balance, or order number. The computer may perform a redaction process that computationally nullifies the import of the named entity in the modeling processes described herein.
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公开(公告)号：US11019201B2
公开(公告)日：2021-05-25
申请号：US16784071
申请日：2020-02-06
Applicant: PINDROP SECURITY, INC.
Inventor： Akanksha , Terry Nelms, II , Kailash Patil , Chirag Tailor , Khaled Lakhdhar
Abstract: Embodiments described herein provide for detecting whether an Automatic Number Identification (ANI) associated with an incoming call is a gateway, according to rules-based models and machine learning models generated by the computer using call data stored in one or more databases.
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