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公开(公告)号：US20240363123A1
公开(公告)日：2024-10-31
申请号：US18646310
申请日：2024-04-25
发明人： Elie KHOURY , Ganesh SIVARAMAN , Tianxiang CHEN , Nikolay GAUBITCH , David LOONEY , Amit GUPTA , Vijay BALASUBRAMANIYAN , Nicholas KLEIN , Anthony STANKUS
摘要： Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.
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公开(公告)号：US20240171680A1
公开(公告)日：2024-05-23
申请号：US18423858
申请日：2024-01-26
发明人： John CORNWELL , Terry NELMS, II
IPC分类号： H04M3/51 , G06F18/214 , H04M3/22 , H04M3/42
CPC分类号： H04M3/5175 , G06F18/214 , H04M3/2218 , H04M3/2281 , H04M3/42059 , G06V2201/10
摘要： 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：US20240169040A1
公开(公告)日：2024-05-23
申请号：US18515128
申请日：2023-11-20
发明人： Hrishikesh RAO , Ricky CASAL , Elie KHOURY , Eric LORIMER , John CORNWELL , Kailash PATIL
IPC分类号： G06F21/31
CPC分类号： G06F21/316
摘要： Embodiments include a computing device that executes software routines and/or one or more machine-learning architectures including a neural network-based embedding extraction system that to produce an embedding vector representing a user's behavior's keypresses, where the system extracts the behaviorprint embedding vector using the keypress features that the system references later for authenticating users. Embodiments may extract and evaluate keypress features, such as keypress sequences, keypress pressure or volume, and temporal keypress features, such as the duration of keypresses and the interval between keypresses, among others. Some embodiments employ a deep neural network architecture that generates a behaviorprint embedding vector representation of the keypress duration and interval features that is used for enrollment and at inference time to authenticate users.
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4.发明授权公开(公告)号：US11948553B2
公开(公告)日：2024-04-02
申请号：US17192464
申请日：2021-03-04
发明人： Kedar Phatak , Elie Khoury
CPC分类号： G10L15/063 , G06N3/045 , G06N20/00 , G10L15/16 , G10L25/27
摘要： Embodiments described herein provide for audio processing operations that evaluate characteristics of audio signals that are independent of the speaker's voice. A neural network architecture trains and applies discriminatory neural networks tasked with modeling and classifying speaker-independent characteristics. The task-specific models generate or extract feature vectors from input audio data based on the trained embedding extraction models. The embeddings from the task-specific models are concatenated to form a deep-phoneprint vector for the input audio signal. The DP vector is a low dimensional representation of the each of the speaker-independent characteristics of the audio signal and applied in various downstream operations.
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公开(公告)号：US20240062753A1
公开(公告)日：2024-02-22
申请号：US18385632
申请日：2023-10-31
发明人： Hrishikesh Rao
IPC分类号： G10L15/197 , G10L15/04 , G10L15/30 , G10L15/22
CPC分类号： G10L15/197 , G10L15/04 , G10L15/30 , G10L15/22 , G10L2015/223 , G10L2015/088
摘要： 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：US11842748B2
公开(公告)日：2023-12-12
申请号：US17121291
申请日：2020-12-14
发明人： Elie Khoury , Matthew Garland
摘要： Methods, systems, and apparatuses for audio event detection, where the determination of a type of sound data is made at the cluster level rather than at the frame level. The techniques provided are thus more robust to the local behavior of features of an audio signal or audio recording. The audio event detection is performed by using Gaussian mixture models (GMMs) to classify each cluster or by extracting an i-vector from each cluster. Each cluster may be classified based on an i-vector classification using a support vector machine or probabilistic linear discriminant analysis. The audio event detection significantly reduces potential smoothing error and avoids any dependency on accurate window-size tuning. Segmentation may be performed using a generalized likelihood ratio and a Bayesian information criterion, and the segments may be clustered using hierarchical agglomerative clustering. Audio frames may be clustered using K-means and GMMs.
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公开(公告)号：US11756564B2
公开(公告)日：2023-09-12
申请号：US16442279
申请日：2019-06-14
发明人： Ganesh Sivaraman , Elie Khoury
IPC分类号： G10L21/0232 , G10L25/30 , G06N3/048
CPC分类号： G10L21/0232 , G06N3/048 , G10L25/30
摘要： A computer may segment a noisy audio signal into audio frames and execute a deep neural network (DNN) to estimate an instantaneous function of clean speech spectrum and noisy audio spectrum in the audio frame. This instantaneous function may correspond to a ratio of an a-priori signal to noise ratio (SNR) and an a-posteriori SNR of the audio frame. The computer may add estimated instantaneous function to the original noisy audio frame to output an enhanced speech audio frame.
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公开(公告)号：US20230007120A1
公开(公告)日：2023-01-05
申请号：US17943893
申请日：2022-09-13
发明人： Lance Douglas
摘要： Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner. Other aspects utilize inner edge data from an intermediate node of the communications network which may be analyzed against other inner edge data from other intermediate nodes and/or outer edge data.
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公开(公告)号：US20220301569A1
公开(公告)日：2022-09-22
申请号：US17746832
申请日：2022-05-17
发明人： Elie KHOURY , Matthew GARLAND
IPC分类号： G10L17/26 , H04L9/40 , G06F21/32 , G06K9/62 , G10L25/30 , G10L17/18 , G10L17/04 , G10L15/26 , G06V40/10 , G06V40/16
摘要： A score indicating a likelihood that a first subject is the same as a second subject may be calibrated to compensate for aging of the first subject between samples of age-sensitive biometric characteristics. Age of the first subject obtained at a first sample time and age of the second subject obtained at a second sample time may be averaged, and an age approximation may be generated based on at least the age average and an interval between the first and second samples. The age approximation, the interval between the first and second sample times, and an obtained gender of the subject are used to calibrate the likelihood score.
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公开(公告)号：US11445060B2
公开(公告)日：2022-09-13
申请号：US16927464
申请日：2020-07-13
发明人： Lance Douglas
摘要： Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner. Other aspects utilize inner edge data from an intermediate node of the communications network which may be analyzed against other inner edge data from other intermediate nodes and/or outer edge data.
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