Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systems

Understand

In this paper, we propose a high-quality generative text-to-speech (TTS) system using an effective spectrum and excitation estimation method.

• Our previous research verified the effectiveness of the ExcitNet-based speech generation model in a parametric TTS framework.
• However, the challenge remains to build a high-quality speech synthesis system because auxiliary conditional features estimated by a simple deep neural network often contain large prediction errors, and the errors are inevitably propagated throughout the autoregressive generation process of the ExcitNet vocoder.
• To generate more natural speech signals, we exploited a sequence-to-sequence (seq2seq) acoustic model with an attention-based generative network (e.g., Tacotron 2) to estimate the condition parameters of the ExcitNet vocoder.