A Comparison of Expressive Speech Synthesis Approaches based on Neural Network
Liumeng Xue, Xiaolian Zhu, Xiaochun An, Lei Xie · 2018
Adaptability and controllability in changing speaking styles and speaker characteristics are the advantages of deep neural networks (DNNs) based statistical parametric speech synthesis (SPSS). This paper presents a comprehensive study on the use of DNNs for expressive speech synthesis with a small set of emotional speech data. Specifically, we study three typical model adaptation approaches: (1) retraining a neural model by emotion-specific data (retrain), (2) augmenting the network input using emotion-specific codes (code) and (3) using emotion-dependent output layers with shared hidden layers (multi-head). Long-short term memory (LSTM) networks are used as the acoustic models. Objective and subjective evaluations have demonstrated that the multi-head approach consistently outperforms the other two approaches with more natural emotion delivered in the synthesized speech.