Importance

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Abstract

The present disclosure provides a model training method, including: performing feature extraction from a speech sample to obtain a speech feature; inputting the speech feature into an encoding network of a to-be-trained model for encoding processing; decoding an intermediate encoding feature to obtain an additional loss; obtaining an encoding loss based on an encoding feature and an encoding label; obtaining a total encoding loss based on the additional loss, the encoding loss, and a preset first loss weight; inputting the encoding feature into a decoding network for decoding processing to obtain a total decoding loss; obtaining a total model loss based on the total encoding loss, the total decoding loss, and a preset second loss weight; updating parameters in the model based on the total model loss, and continuing to train the to-be-trained model according to the updated parameters until the total model loss converges, obtaining a trained model.

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First Claim