Personalizing a dnn-based text-to-speech system using small target speech corpus

Abstract

A personalized text-to-speech system configured to perform speaker adaption is disclosed. The TTS system includes an acoustic model comprising a base neural network and a differential neural network. The base neural network is configured to generate acoustic parameters corresponding to a base speaker or voice actor, while the differential neural network is configured to generate acoustic parameters corresponding to differences between acoustic parameters of the base speaker and a particular target speaker. The output of the acoustic model is then a weighted linear combination of the output from the base neural network and differential neural network. The base neural network and differential neural network share a first input layer and first plurality of hidden layers. Thereafter, the base neural network further comprises a second plurality of hidden layers and output layer. In parallel, the differential neural network further comprises a third plurality of hidden layers and separate output layer.