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公开(公告)号：US20200152207A1
公开(公告)日：2020-05-14
申请号：US16617219
申请日：2019-04-15
Applicant: Google LLC
Inventor： Quan Wang , Yash Sheth , Ignacio Lopez Moreno , Li Wan
Abstract: Techniques are described for training and/or utilizing an end-to-end speaker diarization model. In various implementations, the model is a recurrent neural network (RNN) model, such as an RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer. Audio features of audio data can be applied as input to an end-to-end speaker diarization model trained according to implementations disclosed herein, and the model utilized to process the audio features to generate, as direct output over the model, speaker diarization results. Further, the end-to-end speaker diarization model can be a sequence-to-sequence model, where the sequence can have variable length. Accordingly, the model can be utilized to generate speaker diarization results for any of various length audio segments.
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公开(公告)号：US11545157B2
公开(公告)日：2023-01-03
申请号：US16617219
申请日：2019-04-15
Applicant: Google LLC
Inventor： Quan Wang , Yash Sheth , Ignacio Lopez Moreno , Li Wan
Abstract: Techniques are described for training and/or utilizing an end-to-end speaker diarization model. In various implementations, the model is a recurrent neural network (RNN) model, such as an RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer. Audio features of audio data can be applied as input to an end-to-end speaker diarization model trained according to implementations disclosed herein, and the model utilized to process the audio features to generate, as direct output over the model, speaker diarization results. Further, the end-to-end speaker diarization model can be a sequence-to-sequence model, where the sequence can have variable length. Accordingly, the model can be utilized to generate speaker diarization results for any of various length audio segments.
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