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    Dimensionality reduction of baum-welch statistics for speaker recognition
    2.
    发明授权
    Dimensionality reduction of baum-welch statistics for speaker recognition 有权

    公开(公告)号：US10553218B2

    公开(公告)日：2020-02-04

    申请号：US15709232

    申请日：2017-09-19

    Applicant: PINDROP SECURITY, INC.

    Inventor： Elie Khoury ,  Matthew Garland

    IPC: G10L17/00 ,  G10L17/18 ,  G10L17/02 ,  G10L17/06

    Abstract: In a speaker recognition apparatus, audio features are extracted from a received recognition speech signal, and first order Gaussian mixture model (GMM) statistics are generated therefrom based on a universal background model that includes a plurality of speaker models. The first order GMM statistics are normalized with regard to a duration of the received speech signal. The deep neural network reduces a dimensionality of the normalized first order GMM statistics, and outputs a voiceprint corresponding to the recognition speech signal.

    System and method for cluster-based audio event detection
    3.
    发明授权
    System and method for cluster-based audio event detection 有权

    公开(公告)号：US11842748B2

    公开(公告)日：2023-12-12

    申请号：US17121291

    申请日：2020-12-14

    Applicant: PINDROP SECURITY, INC.

    Inventor： Elie Khoury ,  Matthew Garland

    IPC: G10L25/45 ,  G10L25/51 ,  G10L25/27 ,  G10L25/78

    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.

    System and method for cluster-based audio event detection
    5.
    发明授权
    System and method for cluster-based audio event detection 有权

    公开(公告)号：US10141009B2

    公开(公告)日：2018-11-27

    申请号：US15610378

    申请日：2017-05-31

    Applicant: PINDROP SECURITY, INC.

    Inventor： Elie Khoury ,  Matthew Garland

    IPC: G10L25/45 ,  G10L25/51 ,  G10L25/27 ,  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.

    Channel-compensated low-level features for speaker recognition
    8.
    发明授权
    Channel-compensated low-level features for speaker recognition 有权

    公开(公告)号：US11657823B2

    公开(公告)日：2023-05-23

    申请号：US17107496

    申请日：2020-11-30

    Applicant: PINDROP SECURITY, INC.

    Inventor： Elie Khoury ,  Matthew Garland

    IPC: G10L17/20 ,  G10L17/02 ,  G10L17/04 ,  G10L17/18 ,  G10L19/028

    CPC classification number: G10L17/20 ,  G10L17/02 ,  G10L17/04 ,  G10L17/18 ,  G10L19/028

    Abstract: A system for generating channel-compensated features of a speech signal includes a channel noise simulator that degrades the speech signal, a feed forward convolutional neural network (CNN) that generates channel-compensated features of the degraded speech signal, and a loss function that computes a difference between the channel-compensated features and handcrafted features for the same raw speech signal. Each loss result may be used to update connection weights of the CNN until a predetermined threshold loss is satisfied, and the CNN may be used as a front-end for a deep neural network (DNN) for speaker recognition/verification. The DNN may include convolutional layers, a bottleneck features layer, multiple fully-connected layers and an output layer. The bottleneck features may be used to update connection weights of the convolutional layers, and dropout may be applied to the convolutional layers.

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