公开(公告)号：US20240169040A1

公开(公告)日：2024-05-23

申请号：US18515128

申请日：2023-11-20

Applicant: PINDROP SECURITY, INC.

Inventor： Hrishikesh RAO ,  Ricky CASAL ,  Elie KHOURY ,  Eric LORIMER ,  John CORNWELL ,  Kailash PATIL

IPC: G06F21/31

CPC classification number: G06F21/316

Abstract: 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.

公开(公告)号：US20200243077A1

公开(公告)日：2020-07-30

申请号：US16775149

申请日：2020-01-28

Applicant: PINDROP SECURITY, INC.

Inventor： Hrishikesh RAO

IPC: G10L15/197 ,  G10L15/04 ,  G10L15/22 ,  G10L15/30

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.

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：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.