公开(公告)号：US20240363125A1

公开(公告)日：2024-10-31

申请号：US18646493

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: G10L17/26 ,  G10L17/02 ,  G10L17/04 ,  G10L25/60 ,  H04M3/22 ,  H04M3/51

CPC classification number: G10L17/26 ,  G10L17/02 ,  G10L17/04 ,  G10L25/60 ,  H04M3/2281 ,  H04M3/5183 ,  H04M2201/405

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：US20240355334A1

公开(公告)日：2024-10-24

申请号：US18388457

申请日：2023-11-09

Applicant: PINDROP SECURITY, INC.

Inventor： Umair Altaf ,  Sai Pradeep PERI ,  Lakshay PHATELA ,  Payas GUPTA ,  Yitao SUN ,  Svetlana AFANASEVA ,  Kailash PATIL ,  Elie KHOURY ,  Bradley MAGNETTA ,  Vijay BALASUBRAMANIYAN ,  Tianxiang CHEN

IPC: G10L17/06

CPC classification number: G10L17/06

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US20240355323A1

公开(公告)日：2024-10-24

申请号：US18388447

申请日：2023-11-09

Applicant: PINDROP SECURITY, INC.

Inventor： Umair Altaf ,  Sai Pradeep PERI ,  Lakshay PHATELA ,  Payas GUPTA ,  Yitao SUN ,  Svetlane AFANASEVA ,  Kailash PATIL ,  Elie KHOURY ,  Bradley MAGNETTA ,  Vijay BALASUBRAMANIYAN ,  Tianxiang CHEN

IPC: G10L15/16 ,  G10L15/02

CPC classification number: G10L15/16 ,  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. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

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

IPC: G10L17/04 ,  G10L17/10

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.

公开(公告)号：US20250037507A1

公开(公告)日：2025-01-30

申请号：US18919049

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：US20240363123A1

公开(公告)日：2024-10-31

申请号：US18646310

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: G10L17/26 ,  G10L15/02

CPC classification number: G10L17/26 ,  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.

公开(公告)号：US20210110813A1

公开(公告)日：2021-04-15

申请号：US17066210

申请日：2020-10-08

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Amruta VIDWANS

IPC: G10L15/16 ,  G10L15/06 ,  G10L17/04 ,  G10L25/51

Abstract: Described herein are systems and methods for improved audio analysis using a computer-executed neural network having one or more in-network data augmentation layers. The systems described herein help ease or avoid unwanted strain on computing resources by employing the data augmentation techniques within the layers of the neural network. The in-network data augmentation layers will produce various types of simulated audio data when the computer applies the neural network on an inputted audio signal during a training phase, enrollment phase, and/or testing phase. Subsequent layers of the neural network (e.g., convolutional layer, pooling layer, data augmentation layer) ingest the simulated audio data and the inputted audio signal and perform various operations.

公开(公告)号：US20240363124A1

公开(公告)日：2024-10-31

申请号：US18646431

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: G10L17/26 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08

CPC classification number: G10L17/26 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08

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：US20240355336A1

公开(公告)日：2024-10-24

申请号：US18439049

申请日：2024-02-12

Applicant: PINDROP SECURITY, INC.

Inventor： Umair ALTAF ,  Sai Pradeep PERI ,  Lakshay PHATELA ,  Payas GUPTA ,  Yitao SUN ,  Svetlana AFANASEVA ,  Kailash PATIL ,  Elie KHOURY ,  Bradley MAGNETTA ,  Vijay BALASUBRAMANIYAN ,  Tianxiang CHEN

IPC: G10L17/18 ,  G10L15/26

CPC classification number: G10L17/18 ,  G10L15/26

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US20220121868A1

公开(公告)日：2022-04-21

申请号：US17503152

申请日：2021-10-15

Applicant: Pindrop Security, Inc.

Inventor： Tianxiang CHEN ,  Elie KHOURY

IPC: G06K9/00 ,  G06K9/62 ,  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.