公开(公告)号：US11715460B2

公开(公告)日：2023-08-01

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 ,  G10L25/51 ,  G10L17/04 ,  G10L15/06

CPC classification number: G10L15/16 ,  G10L15/063 ,  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：US20190385630A1

公开(公告)日：2019-12-19

申请号：US16442279

申请日：2019-06-14

Applicant: PINDROP SECURITY, INC.

Inventor： Ganesh Sivaraman ,  Elie Khoury

IPC: G10L21/0232 ,  G10L25/30 ,  G06N3/04

Abstract: A computer may segment a noisy audio signal into audio frames and execute a deep neural network (DNN) to estimate an instantaneous function of clean speech spectrum and noisy audio spectrum in the audio frame. This instantaneous function may correspond to a ratio of an a-priori signal to noise ratio (SNR) and an a-posteriori SNR of the audio frame. The computer may add estimated instantaneous function to the original noisy audio frame to output an enhanced speech audio frame.

公开(公告)号：US12266368B2

公开(公告)日：2025-04-01

申请号：US17165180

申请日：2021-02-02

Applicant: PINDROP SECURITY, INC.

Inventor： Ganesh Sivaraman ,  Elie Khoury ,  Avrosh Kumar

IPC: G10L17/04 ,  G06N3/04 ,  G10L17/18

Abstract: Embodiments described herein provide for systems and methods for voice-based cross-channel enrollment and authentication. The systems control for and mitigate against variations in audio signals received across any number of communications channels by training and employing a neural network architecture comprising a speaker verification neural network and a bandwidth expansion neural network. The bandwidth expansion neural network is trained on narrowband audio signals to produce and generate estimated wideband audio signals corresponding to the narrowband audio signals. These estimated wideband audio signals may be fed into one or more downstream applications, such as the speaker verification neural network or embedding extraction neural network. The speaker verification neural network can then compare and score inbound embeddings for a current call against enrolled embeddings, regardless of the channel used to receive the inbound signal or enrollment signal.

公开(公告)号：US11756564B2

公开(公告)日：2023-09-12

申请号：US16442279

申请日：2019-06-14

Applicant: PINDROP SECURITY, INC.

Inventor： Ganesh Sivaraman ,  Elie Khoury

IPC: G10L21/0232 ,  G10L25/30 ,  G06N3/048

CPC classification number: G10L21/0232 ,  G06N3/048 ,  G10L25/30

Abstract: A computer may segment a noisy audio signal into audio frames and execute a deep neural network (DNN) to estimate an instantaneous function of clean speech spectrum and noisy audio spectrum in the audio frame. This instantaneous function may correspond to a ratio of an a-priori signal to noise ratio (SNR) and an a-posteriori SNR of the audio frame. The computer may add estimated instantaneous function to the original noisy audio frame to output an enhanced speech audio frame.