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    ROBUST SPOOFING DETECTION SYSTEM USING DEEP RESIDUAL NEURAL NETWORKS
    42.
    发明公开
    ROBUST SPOOFING DETECTION SYSTEM USING DEEP RESIDUAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240153510A1

    公开(公告)日：2024-05-09

    申请号：US18394300

    申请日：2023-12-22

    Applicant: PINDROP SECURITY, INC.

    Inventor： Tianxiang CHEN ,  Elie KHOURY

    IPC: G10L17/18 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08 ,  G10L17/22

    CPC classification number: G10L17/18 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08 ,  G10L17/22

    Abstract: Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.

    LIMITING IDENTITY SPACE FOR VOICE BIOMETRIC AUTHENTICATION
    43.
    发明申请
    LIMITING IDENTITY SPACE FOR VOICE BIOMETRIC AUTHENTICATION 有权

    公开(公告)号：US20220392452A1

    公开(公告)日：2022-12-08

    申请号：US17832146

    申请日：2022-06-03

    Applicant: Pindrop Security, Inc.

    Inventor： Payas GUPTA ,  Elie KHOURY ,  Terry NELMS, II ,  Vijay BALASUBRAMANIYAN

    IPC: G10L17/04 ,  G06F21/32 ,  G10L17/22

    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.

    SPEAKER RECOGNITION WITH QUALITY INDICATORS
    44.
    发明申请
    SPEAKER RECOGNITION WITH QUALITY INDICATORS 有权

    公开(公告)号：US20220059121A1

    公开(公告)日：2022-02-24

    申请号：US17408281

    申请日：2021-08-20

    Applicant: PINDROP SECURITY, INC.

    Inventor： Hrishikesh RAO ,  Kedar PHATAK ,  Elie KHOURY

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

    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.

    SYSTEMS AND METHODS FOR END-TO-END ARCHITECTURES FOR VOICE SPOOFING DETECTION
    45.
    发明申请
    SYSTEMS AND METHODS FOR END-TO-END ARCHITECTURES FOR VOICE SPOOFING DETECTION 审中-公开

    公开(公告)号：US20200322377A1

    公开(公告)日：2020-10-08

    申请号：US16841473

    申请日：2020-04-06

    Applicant: PINDROP SECURITY, INC.

    Inventor： Khaled LAKHDHAR ,  Parav NAGARSHETH ,  Tianxiang CHEN ,  Elie KHOURY

    IPC: H04L29/06 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20 ,  G06F17/18 ,  G10L19/26

    Abstract: Embodiments described herein provide for automatically detecting whether an audio signal is a spoofed audio signal or a genuine audio signal. A spoof detection system can include an audio signal transforming front end and a classification back end. Both the front end and the back end can include neural networks that can be trained using the same set of labeled audio signals. The audio signal transforming front end can include a one or more neural networks for per-channel energy normalization transformation of the audio signal, and the back end can include a convolution neural network for classification into spoofed or genuine audio signal. In some embodiments, the transforming audio signal front end can include one or more neural networks for bandpass filtering of the audio signals, and the back end can include a residual neural network for audio signal classification into spoofed or genuine audio signal.

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