Systems and methods for generating multi-language media content with automatic selection of matching voices

    公开(公告)号:US12086558B2

    公开(公告)日:2024-09-10

    申请号:US17196285

    申请日:2021-03-09

    CPC classification number: G06F40/47 G06F40/58 G10L15/005 G10L15/16

    Abstract: A method and system for automated voice casting compares candidate voices samples from candidate speakers in a target language with a primary voice sample from a primary speaker in a primary language. Utterances in the audio samples of the candidates speakers and the primary speaker are identified and typed and voice samples generated that meet applicable utterance type criteria. A neural network is used to generate an embedding for the voice samples. A voice sample can include groups of different utterance types and embeddings generated for each utterance group in the voice sample and then combined in a weighted form wherein the resulting embedding emphasizes selected utterance types. Similarities between embeddings for the candidate voice samples relative to the primary voice sample are evaluated and used to select a candidate speaker that is a vocal match.

    Systems and methods to maintain user privacy whtle providing recommendations

    公开(公告)号:US11907401B2

    公开(公告)日:2024-02-20

    申请号:US16919056

    申请日:2020-07-01

    CPC classification number: G06F21/6254 G06F16/9535 G06F21/6263

    Abstract: A systematic method of introducing obfuscating “organic” noise to a user's content engagement history leverages a recommender system by creating a public history on a client device which is a superset of the user's true engagement history. The method builds up the superset history over time through a client's interaction with the recommender system by simulating organic growth in a user's actual engagement history. The organic superset prevents an adversary with access to the underlying recommendation model from readily distinguishing between signal and noise in a user's query and obfuscates the user's engagement history with the recommender system.

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