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1.发明授权公开(公告)号：US11948553B2
公开(公告)日：2024-04-02
申请号：US17192464
申请日：2021-03-04
发明人： Kedar Phatak , Elie Khoury
CPC分类号： G10L15/063 , G06N3/045 , G06N20/00 , G10L15/16 , G10L25/27
摘要： Embodiments described herein provide for audio processing operations that evaluate characteristics of audio signals that are independent of the speaker's voice. A neural network architecture trains and applies discriminatory neural networks tasked with modeling and classifying speaker-independent characteristics. The task-specific models generate or extract feature vectors from input audio data based on the trained embedding extraction models. The embeddings from the task-specific models are concatenated to form a deep-phoneprint vector for the input audio signal. The DP vector is a low dimensional representation of the each of the speaker-independent characteristics of the audio signal and applied in various downstream operations.
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公开(公告)号：US11842748B2
公开(公告)日：2023-12-12
申请号：US17121291
申请日：2020-12-14
发明人： Elie Khoury , Matthew Garland
摘要： 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.
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公开(公告)号：US11756564B2
公开(公告)日：2023-09-12
申请号：US16442279
申请日：2019-06-14
发明人： Ganesh Sivaraman , Elie Khoury
IPC分类号： G10L21/0232 , G10L25/30 , G06N3/048
CPC分类号： G10L21/0232 , G06N3/048 , G10L25/30
摘要： 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.
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公开(公告)号：US10672403B2
公开(公告)日：2020-06-02
申请号：US15890967
申请日：2018-02-07
发明人： Elie Khoury , Matthew Garland
IPC分类号： G10L17/26 , G10L15/26 , H04L29/06 , G06K9/00 , G06F21/32 , G06K9/62 , G10L25/30 , G10L17/18 , G10L17/04
摘要： A score indicating a likelihood that a first subject is the same as a second subject may be calibrated to compensate for aging of the first subject between samples of age-sensitive biometric characteristics. Age of the first subject obtained at a first sample time and age of the second subject obtained at a second sample time may be averaged, and an age approximation may be generated based on at least the age average and an interval between the first and second samples. The age approximation, the interval between the first and second sample times, and an obtained gender of the subject are used to calibrate the likelihood score.
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公开(公告)号：US10141009B2
公开(公告)日：2018-11-27
申请号：US15610378
申请日：2017-05-31
发明人： Elie Khoury , Matthew Garland
摘要： 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.
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公开(公告)号：US12015637B2
公开(公告)日：2024-06-18
申请号：US16841473
申请日：2020-04-06
发明人： Khaled Lakhdhar , Parav Nagarsheth , Tianxiang Chen , Elie Khoury
IPC分类号： H04L9/40 , G06F17/18 , G06N3/045 , G06N3/084 , G06N20/10 , G10L17/00 , G10L17/04 , G10L17/26 , G10L19/26 , H04L65/75
CPC分类号： H04L63/1466 , G06F17/18 , G06N3/045 , G06N3/084 , G06N20/10 , G10L17/00 , G10L17/04 , G10L17/26 , G10L19/26 , H04L65/75
摘要： 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.
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公开(公告)号：US11862177B2
公开(公告)日：2024-01-02
申请号：US17155851
申请日：2021-01-22
发明人： Tianxiang Chen , Elie Khoury
摘要： Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.
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公开(公告)号：US11657823B2
公开(公告)日：2023-05-23
申请号：US17107496
申请日：2020-11-30
发明人： Elie Khoury , Matthew Garland
IPC分类号： G10L17/20 , G10L17/02 , G10L17/04 , G10L17/18 , G10L19/028
CPC分类号： G10L17/20 , G10L17/02 , G10L17/04 , G10L17/18 , G10L19/028
摘要： 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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公开(公告)号：US11335353B2
公开(公告)日：2022-05-17
申请号：US16889337
申请日：2020-06-01
发明人： Elie Khoury , Matthew Garland
IPC分类号： G10L17/26 , H04L29/06 , G06F21/32 , G06K9/62 , G10L25/30 , G10L17/18 , G10L17/04 , G10L15/26 , G06V40/10 , G06V40/16 , G06V40/50
摘要： A score indicating a likelihood that a first subject is the same as a second subject may be calibrated to compensate for aging of the first subject between samples of age-sensitive biometric characteristics. Age of the first subject obtained at a first sample time and age of the second subject obtained at a second sample time may be averaged, and an age approximation may be generated based on at least the age average and an interval between the first and second samples. The age approximation, the interval between the first and second sample times, and an obtained gender of the subject are used to calibrate the likelihood score.
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公开(公告)号：US10692502B2
公开(公告)日：2020-06-23
申请号：US15910387
申请日：2018-03-02
发明人： Elie Khoury , Parav Nagarsheth , Kailash Patil , Matthew Garland
摘要： 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.
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