公开(公告)号：US20190392815A1

公开(公告)日：2019-12-26

申请号：US16448384

申请日：2019-06-21

Applicant: Genesys Telecommunications Laboratories, Inc.

Inventor： Elluru Veera Raghavendra ,  Aravind Ganapathiraju

IPC: G10L15/06 ,  G10L15/16 ,  G10L15/18 ,  G10L13/04 ,  G06K9/62

Abstract: A system and method are presented for F0 transfer learning for improving F0 prediction with deep neural network models. Larger models are trained using long short-term memory (LSTM) and multi-layer perceptron (MLP) feed-forward hidden layer modeling. The fundamental frequency values for voiced and unvoiced segments are identified and extracted from the larger models. The values for voiced regions are transferred and applied to training a smaller model and the smaller model is applied in the text to speech system for real-time speech synthesis output.

公开(公告)号：US11302307B2

公开(公告)日：2022-04-12

申请号：US16448384

申请日：2019-06-21

Applicant: Genesys Telecommunications Laboratories, Inc.

Inventor： Elluru Veera Raghavendra ,  Aravind Ganapathiraju

IPC: G10L15/16 ,  G10L15/06 ,  G06K9/62 ,  G10L13/04 ,  G10L15/18

Abstract: A system and method are presented for F0 transfer learning for improving F0 prediction with deep neural network models. Larger models are trained using long short-term memory (LSTM) and multi-layer perceptron (MLP) feed-forward hidden layer modeling. The fundamental frequency values for voiced and unvoiced segments are identified and extracted from the larger models. The values for voiced regions are transferred and applied to training a smaller model and the smaller model is applied in the text to speech system for real-time speech synthesis output.