Unit Selection with Hierarchical Cascaded Long Short Term Memory Bidirectional Recurrent Neural Nets

Bidirectional recurrent neural nets have demonstrated state-of-the-art performance for parametric speech synthesis. In this paper, we introduce a top-down application of recurrent neural net models to unit-selection synthesis. A hierarchical cascaded network graph predicts context phone duration, speech unit encoding and frame-level logF0 information that serves as targets for the search of units. The new approach is compared with an existing state-of-art hybrid system that uses Hidden Markov Models as basis for the statistical unit search.