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公开(公告)号：US20200168208A1
公开(公告)日：2020-05-28
申请号：US16085262
申请日：2017-03-22
Applicant: SRI International
Inventor： Vikramjit Mitra , Horacio E. Franco , Chris D. Bartels , Dimitra Vergyri , Julien van Hout , Martin Graciarena
Abstract: Systems and methods for speech recognition are provided. In some aspects, the method comprises receiving, using an input, an audio signal. The method further comprises splitting the audio signal into auditory test segments. The method further comprises extracting, from each of the auditory test segments, a set of acoustic features. The method further comprises applying the set of acoustic features to a deep neural network to produce a hypothesis for the corresponding auditory test segment. The method further comprises selectively performing one or more of: indirect adaptation of the deep neural network and direct adaptation of the deep neural network.
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Patent Agency Ranking
公开(公告)号：US20180214061A1
公开(公告)日：2018-08-02
申请号：US15505577
申请日：2015-08-05
Applicant: SRI International
Inventor： Bruce Knoth , Dimitra Vergyri , Elizabeth Shriberg , Vikramjit Mitra , Mitchell McLaren , Andreas Kathol , Colleen Richey , Martin Graciarena
CPC classification number: A61B5/165 , G06N20/00 , G10L15/1807 , G10L15/1822 , G10L17/26 , G10L25/63 , G10L25/66
Abstract: A computer-implemented method can include a speech collection module collecting a speech pattern from a patient, a speech feature computation module computing at least one speech feature from the collected speech pattern, a mental health determination module determining a state-of-mind of the patient based at least in part on the at least one computed speech feature, and an output module providing an indication of a diagnosis with regard to a possibility that the patient is suffering from a certain condition such as depression or Post-Traumatic Stress Disorder (PTSD).
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公开(公告)号：US11217228B2
公开(公告)日：2022-01-04
申请号：US16085262
申请日：2017-03-22
Applicant: SRI International
Inventor： Vikramjit Mitra , Horacio E. Franco , Chris D. Bartels , Dimitra Vergyri , Julien van Hout , Martin Graciarena
Abstract: Systems and methods for speech recognition are provided. In some aspects, the method comprises receiving, using an input, an audio signal. The method further comprises splitting the audio signal into auditory test segments. The method further comprises extracting, from each of the auditory test segments, a set of acoustic features. The method further comprises applying the set of acoustic features to a deep neural network to produce a hypothesis for the corresponding auditory test segment. The method further comprises selectively performing one or more of: indirect adaptation of the deep neural network and direct adaptation of the deep neural network.
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公开(公告)号：US10777188B2
公开(公告)日：2020-09-15
申请号：US16191296
申请日：2018-11-14
Applicant: SRI International
Inventor： Julien van Hout , Vikramjit Mitra , Horacio Franco , Emre Yilmaz
Abstract: A computing system determines whether a reference audio signal contains a query. A time-frequency convolutional neural network (TFCNN) comprises a time and frequency convolutional layers and a series of additional layers, which include a bottleneck layer. The computation engine applies the TFCNN to samples of a query utterance at least through the bottleneck layer. A query feature vector comprises output values of the bottleneck layer generated when the computation engine applies the TFCNN to the samples of the query utterance. The computation engine also applies the TFCNN to samples of the reference audio signal at least through the bottleneck layer. A reference feature vector comprises output values of the bottleneck layer generated when the computation engine applies the TFCNN to the samples of the reference audio signal. The computation engine determines at least one detection score based on the query feature vector and the reference feature vector.
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5.发明申请公开(公告)号：US20200152179A1
公开(公告)日：2020-05-14
申请号：US16191296
申请日：2018-11-14
Applicant: SRI International
Inventor： Julien van Hout , Vikramjit Mitra , Horacio Franco , Emre Yilmaz
Abstract: A computing system determines whether a reference audio signal contains a query. A time-frequency convolutional neural network (TFCNN) comprises a time and frequency convolutional layers and a series of additional layers, which include a bottleneck layer. The computation engine applies the TFCNN to samples of a query utterance at least through the bottleneck layer. A query feature vector comprises output values of the bottleneck layer generated when the computation engine applies the TFCNN to the samples of the query utterance. The computation engine also applies the TFCNN to samples of the reference audio signal at least through the bottleneck layer. A reference feature vector comprises output values of the bottleneck layer generated when the computation engine applies the TFCNN to the samples of the reference audio signal. The computation engine determines at least one detection score based on the query feature vector and the reference feature vector.
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公开(公告)号：US10478111B2
公开(公告)日：2019-11-19
申请号：US15505577
申请日：2015-08-05
Applicant: SRI International
Inventor： Bruce Knoth , Dimitra Vergyri , Elizabeth Shriberg , Vikramjit Mitra , Mitchell McLaren , Andreas Kathol , Colleen Richey , Martin Graciarena
Abstract: A computer-implemented method can include a speech collection module collecting a speech pattern from a patient, a speech feature computation module computing at least one speech feature from the collected speech pattern, a mental health determination module determining a state-of-mind of the patient based at least in part on the at least one computed speech feature, and an output module providing an indication of a diagnosis with regard to a possibility that the patient is suffering from a certain condition such as depression or Post-Traumatic Stress Disorder (PTSD).
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