公开(公告)号：US20180254046A1

公开(公告)日：2018-09-06

申请号：US15910387

申请日：2018-03-02

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Parav NAGARSHETH ,  Kailash PATIL ,  Matthew GARLAND

IPC: G10L17/02 ,  G10L17/04 ,  G10L17/06 ,  G10L17/18 ,  G10L19/02 ,  G10L25/24

Abstract: An automated speaker verification (ASV) system incorporates a first deep neural network to extract deep acoustic features, such as deep CQCC features, from a received voice sample. The deep acoustic features are processed by a second deep neural network that classifies the deep acoustic features according to a determined likelihood of including a spoofing condition. A binary classifier then classifies the voice sample as being genuine or spoofed.

公开(公告)号：US20170372725A1

公开(公告)日：2017-12-28

申请号：US15610378

申请日：2017-05-31

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Matthew GARLAND

IPC: G10L25/45 ,  G10L25/78 ,  G10L25/27

CPC classification number: G10L25/45 ,  G10L25/27 ,  G10L25/51 ,  G10L25/78

Abstract: Methods, systems, and apparatuses for audio event detection, where the determination of a type of sound data is made at the cluster level rather than at the frame level. The techniques provided are thus more robust to the local behavior of features of an audio signal or audio recording. The audio event detection is performed by using Gaussian mixture models (GMMs) to classify each cluster or by extracting an i-vector from each cluster. Each cluster may be classified based on an i-vector classification using a support vector machine or probabilistic linear discriminant analysis. The audio event detection significantly reduces potential smoothing error and avoids any dependency on accurate window-size tuning. Segmentation may be performed using a generalized likelihood ratio and a Bayesian information criterion, and the segments may be clustered using hierarchical agglomerative clustering. Audio frames may be clustered using K-means and GMMs.