UnitNet: A Sequence-to-Sequence Acoustic Model for Concatenative Speech Synthesis

Abstract

This paper presents UnitNet, a sequence-to-sequence (Seq2Seq) acoustic model for concatenative speech synthesis. Comparing with the Tacotron2 model for Seq2Seq speech synthesis, UnitNet utilizes the phone boundaries of training data and its decoder contains autoregressive structures at both phone and frame levels. This hierarchical architecture can not only extract embedding vectors for representing phone-sized units in the corpus but also measure the dependency among consecutive units, which makes the UnitNet model capable of guiding the selection of phone-sized units for concatenative speech synthesis. A byproduct of this model is that it can also be applied to statistical parametric speech synthesis (SPSS) and improve the robustness of Seq2Seq acoustic feature prediction since it adopts interpretable transition probability prediction rather than attention mechanism for frame-level alignment. Experimental results show that our UnitNet-based concatenative speech synthesis method not only outperforms the unit selection methods using hidden Markov models and Tacotron-based unit embeddings, but also achieves better naturalness and faster inference speed than the SPSS method using FastSpeech and Parallel WaveGAN. Besides, the UnitNet-based SPSS method makes fewer synthesis errors than Tacotron2 and FastSpeech without naturalness degradation.