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

IPC: G10L17/08 ,  G10L15/16 ,  G10L17/02 ,  G10L17/04 ,  G10L17/22 ,  G06N3/04 ,  G06N3/08 ,  G10L17/18

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.

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

IPC: G10L17/04 ,  G10L17/12 ,  G06F21/32

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.

公开(公告)号：US20210082439A1

公开(公告)日：2021-03-18

申请号：US17107496

申请日：2020-11-30

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Matthew GARLAND

IPC: 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.

公开(公告)号：US20200302939A1

公开(公告)日：2020-09-24

申请号：US16895750

申请日：2020-06-08

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Matthew GARLAND

IPC: G10L17/00 ,  H04M1/27 ,  G10L17/24 ,  G10L15/19 ,  G10L17/08 ,  G06N7/00 ,  G10L15/07 ,  G10L15/26 ,  G10L17/04

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.

公开(公告)号：US20180254046A1

公开(公告)日：2018-09-06

申请号：US15910387

申请日：2018-03-02

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Parav NAGARSHETH ,  Kailash PATIL ,  Matthew GARLAND

IPC: G10L17/02 ,  G10L17/04 ,  G10L17/06 ,  G10L17/18 ,  G10L19/02 ,  G10L25/24

Abstract: An automated speaker verification (ASV) system incorporates a first deep neural network to extract deep acoustic features, such as deep CQCC features, from a received voice sample. The deep acoustic features are processed by a second deep neural network that classifies the deep acoustic features according to a determined likelihood of including a spoofing condition. A binary classifier then classifies the voice sample as being genuine or spoofed.

公开(公告)号：US20170372725A1

公开(公告)日：2017-12-28

申请号：US15610378

申请日：2017-05-31

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Matthew GARLAND

IPC: G10L25/45 ,  G10L25/78 ,  G10L25/27

CPC classification number: G10L25/45 ,  G10L25/27 ,  G10L25/51 ,  G10L25/78

Abstract: 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.

公开(公告)号：US20250124945A1

公开(公告)日：2025-04-17

申请号：US18989690

申请日：2024-12-20

Applicant: PINDROP SECURITY, INC.

Inventor： Hrishikesh RAO ,  Kedar PHATAK ,  Elie KHOURY

IPC: G10L25/60 ,  G06N20/20 ,  G10L15/02

Abstract: Embodiments described herein provide for a machine-learning architecture for modeling quality measures for enrollment signals. Modeling these enrollment signals enables the machine-learning architecture to identify deviations from expected or ideal enrollment signal in future test phase calls. These differences can be used to generate quality measures for the various audio descriptors or characteristics of audio signals. The quality measures can then be fused at the score-level with the speaker recognition's embedding comparisons for verifying the speaker. Fusing the quality measures with the similarity scoring essentially calibrates the speaker recognition's outputs based on the realities of what is actually expected for the enrolled caller and what was actually observed for the current inbound caller.

公开(公告)号：US20250037506A1

公开(公告)日：2025-01-30

申请号：US18918928

申请日：2024-10-17

Applicant: Pindrop Security, Inc.

Inventor： Tianxiang CHEN ,  Elie KHOURY

IPC: G06V40/40 ,  G06F18/21 ,  G06F18/22 ,  G06V20/40 ,  G06V40/16 ,  G06V40/70 ,  G10L17/22

Abstract: The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person. This enables the machine-learning architecture to perform identity recognition and verification, and deepfake detection, in an integrated fashion, for both audio data and visual data.

公开(公告)号：US20240363119A1

公开(公告)日：2024-10-31

申请号：US18646375

申请日：2024-04-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Nikolay GAUBITCH ,  David LOONEY ,  Amit GUPTA ,  Vijay BALASUBRAMANIYAN ,  Nicholas KLEIN ,  Anthony STANKUS

IPC: G10L17/00

CPC classification number: G10L17/00

Abstract: 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.

公开(公告)号：US20240363100A1

公开(公告)日：2024-10-31

申请号：US18646228

申请日：2024-04-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Nikolay GAUBITCH ,  David LOONEY ,  Amit GUPTA ,  Vijay BALASUBRAMANIYAN ,  Nicholas KLEIN ,  Anthony STANKUS

IPC: G10L15/02

CPC classification number: G10L15/02

Abstract: 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.