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    Emotion detection using speaker baseline
    3.
    发明授权
    Emotion detection using speaker baseline 有权

    公开(公告)号：US11545174B2

    公开(公告)日：2023-01-03

    申请号：US17178844

    申请日：2021-02-18

    Applicant: Amazon Technologies, Inc.

    Inventor： Daniel Kenneth Bone ,  Chao Wang ,  Viktor Rozgic

    IPC: G10L25/63 ,  G10L17/04

    Abstract: Described herein is a system for emotion detection in audio data using a speaker's baseline. The baseline may represent a user's speaking style in a neutral emotional state. The system is configured to compare the user's baseline with input audio representing speech from the user to determine a emotion of the user. The system may store multiple baselines for the user, each associated with a different context (e.g., environment, activity, etc.), and select one of the baselines to compare with the input audio based on the contextual situation.

    EMOTION DETECTION USING SPEAKER BASELINE
    4.
    发明申请
    EMOTION DETECTION USING SPEAKER BASELINE 有权

    公开(公告)号：US20210249035A1

    公开(公告)日：2021-08-12

    申请号：US17178844

    申请日：2021-02-18

    Applicant: Amazon Technologies, Inc.

    Inventor： Daniel Kenneth Bone ,  Chao Wang ,  Viktor Rozgic

    IPC: G10L25/63 ,  G10L17/04

    Abstract: Described herein is a system for emotion detection in audio data using a speaker's baseline. The baseline may represent a user's speaking style in a neutral emotional state. The system is configured to compare the user's baseline with input audio representing speech from the user to determine a emotion of the user. The system may store multiple baselines for the user, each associated with a different context (e.g., environment, activity, etc.), and select one of the baselines to compare with the input audio based on the contextual situation.

    Self-supervised federated learning
    8.
    发明授权
    Self-supervised federated learning 有权

    公开(公告)号：US12039998B1

    公开(公告)日：2024-07-16

    申请号：US17665129

    申请日：2022-02-04

    Applicant: Amazon Technologies, Inc.

    Inventor： Chieh-Chi Kao ,  Qingming Tang ,  Ming Sun ,  Viktor Rozgic ,  Spyridon Matsoukas ,  Chao Wang

    IPC: G10L25/78 ,  G06N3/045 ,  G06N3/08 ,  G10L25/21

    CPC classification number: G10L25/78 ,  G06N3/045 ,  G06N3/08 ,  G10L25/21

    Abstract: An acoustic event detection system may employ self-supervised federated learning to update encoder and/or classifier machine learning models. In an example operation, an encoder may be pre-trained to extract audio feature data from an audio signal. A decoder may be pre-trained to predict a subsequent portion of audio data (e.g., a subsequent frame of audio data represented by log filterbank energies). The encoder and decoder may be trained using self-supervised learning to improve the decoder's predictions and, by extension, the quality of the audio feature data generated by the encoder. The system may apply federated learning to share encoder updates across user devices. The system may fine-tune the classifier to improve inferences based on the improved audio feature data. The system may distribute classifier updates to the user device(s) to update the on-device classifier.

    Media presence detection
    9.
    发明授权
    Media presence detection 有权

    公开(公告)号：US11069352B1

    公开(公告)日：2021-07-20

    申请号：US16278440

    申请日：2019-02-18

    Applicant: Amazon Technologies, Inc.

    Inventor： Qingming Tang ,  Ming Sun ,  Chieh-Chi Kao ,  Chao Wang ,  Viktor Rozgic

    IPC: G10L15/22 ,  G10L25/78 ,  G10L15/16 ,  G10L15/02

    Abstract: Described herein is a system for media presence detection in audio. The system analyzes audio data to recognize whether a given audio segment contains sounds from a media source as a way of differentiating recorded media source sounds from other live sounds. In exemplary embodiments, the system includes a hierarchical model architecture for processing audio data segments, where individual audio data segments are processed by a trained machine learning model operating locally, and another trained machine learning model provides historical and contextual information to determine a score indicating the likelihood that the audio data segment contains sounds from a media source.

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