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.

公开(公告)号：US20210281680A1

公开(公告)日：2021-09-09

申请号：US17317559

申请日：2021-05-11

Applicant: PINDROP SECURITY, INC.

Inventor： Payas GUPTA ,  Terry NELMS, II

IPC: H04M3/436 ,  H04M3/22 ,  H04M3/42 ,  H04M19/04

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an authentication server for caller ID verification. When a caller makes a phone call, the server receives the phone call and verifies whether the phone call is from a registered device associated with the phone number. The server queries the registered device to retrieve one or more current call states via an authentication function on the registered device. The server compares the states and/or state transitions to the observed states and/or state transitions of the phone call. If the registered device states and/or state transitions match the observed phone call states and/or state transitions, the server verifies that the phone call is from the registered device and not some imposter's device. If there is no such match, the server rejects the phone call before the call phone is connected or terminates the phone call after the phone call is connected.

公开(公告)号：US11019203B2

公开(公告)日：2021-05-25

申请号：US16287879

申请日：2019-02-27

Applicant: PINDROP SECURITY, INC.

Inventor： Payas Gupta ,  Terry Nelms, II

IPC: H04M3/436 ,  H04M3/22 ,  H04M3/42 ,  H04M19/04

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an authentication server for caller ID verification. When a caller makes a phone call, the server receives the phone call and verifies whether the phone call is from a registered device associated with the phone number. The server queries the registered device to retrieve one or more current call states via an authentication function on the registered device. The server compares the states and/or state transitions to the observed states and/or state transitions of the phone call. If the registered device states and/or state transitions match the observed phone call states and/or state transitions, the server verifies that the phone call is from the registered device and not some imposter's device. If there is no such match, the server rejects the phone call before the call phone is connected or terminates the phone call after the phone call is connected.

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.

公开(公告)号：US20200312313A1

公开(公告)日：2020-10-01

申请号：US16829705

申请日：2020-03-25

Applicant: PINDROP SECURITY, INC.

Inventor： Vinay MADDALI ,  David LOONEY ,  Kailash PATIL

IPC: G10L15/197 ,  H04M3/51 ,  G10L15/18 ,  G10L15/02 ,  G10L15/04 ,  G10L25/21 ,  G10L15/06 ,  G10L15/22 ,  G10L25/84 ,  G06N20/00 ,  G06N5/04

Abstract: Embodiments described herein provide for automatically classifying the types of devices that place calls to a call center. A call center system can detect whether an incoming call originated from voice assistant device using trained classification models received from a call analysis service. Embodiments described herein provide for methods and systems in which a computer executes machine learning algorithms that programmatically train (or otherwise generate) global or tailored classification models based on the various types of features of an audio signal and call data. A classification model is deployed to one or more call centers, where the model is used by call center computers executing classification processes for determining whether incoming telephone calls originated from a voice assistant device, such as Amazon Alexa® and Google Home®, or another type of device (e.g., cellular/mobile phone, landline phone, VoIP).

公开(公告)号：US20200259954A1

公开(公告)日：2020-08-13

申请号：US16859370

申请日：2020-04-27

Applicant: PINDROP SECURITY, INC.

Inventor： Payas GUPTA ,  Terry NELMS, II

IPC: H04M3/436 ,  H04M3/42 ,  H04W4/16 ,  H04W12/06

Abstract: In an illustrative embodiment, a user device may block all the phone numbers used by an enterprise. When an enterprise wants to call the user, the enterprise may notify the user device through a separate secure channel that an enterprise phone number is in the process of making a phone call to the user device. The secure channel may include an authentication server that may request the user device to unblock the enterprise phone number. An incoming phone call from the enterprise phone number therefore can be trusted. After the phone call is terminated, the user device may again block the enterprise phone number. An attacker may not have access to the authentication server and a phone call from the attacker with a spoofed enterprise phone number (now blocked) may be dropped by the user device.

公开(公告)号：US10715656B2

公开(公告)日：2020-07-14

申请号：US16522450

申请日：2019-07-25

Applicant: PINDROP SECURITY, INC.

Inventor： Lance Douglas

IPC: H04M15/00 ,  H04M17/00 ,  H04M3/22 ,  H04M3/436 ,  H04M7/00 ,  H04L29/06

Abstract: 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.

公开(公告)号：US20190311730A1

公开(公告)日：2019-10-10

申请号：US16375785

申请日：2019-04-04

Applicant: PINDROP SECURITY, INC.

Inventor： David LOONEY ,  Nikolay D. GAUBITCH

IPC: G10L25/51 ,  G10L25/90 ,  G10L15/22 ,  G10L15/06

Abstract: A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

公开(公告)号：US20190304468A1

公开(公告)日：2019-10-03

申请号：US16442368

申请日：2019-06-14

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Matthew GARLAND

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

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：US20190238956A1

公开(公告)日：2019-08-01

申请号：US16378286

申请日：2019-04-08

Applicant: PINDROP SECURITY, INC.

Inventor： Nick GAUBITCH ,  Scott STRONG ,  John CORNWELL ,  Hassan KINGRAVI ,  David DEWEY

IPC: H04Q3/70 ,  H04L25/02 ,  H04M3/493 ,  H04M3/22 ,  H04M7/12 ,  H04Q1/45 ,  G10L25/51

CPC classification number: H04Q3/70 ,  G10L25/51 ,  H04L25/0202 ,  H04M3/2281 ,  H04M3/493 ,  H04M7/1295 ,  H04M2201/18 ,  H04M2203/60 ,  H04Q1/45 ,  H04Q2213/13139 ,  H04Q2213/13405 ,  H04Q2213/13515

Abstract: Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.