Articulatory Movement Prediction Using Deep Bidirectional Long Short-Term Memory Based Recurrent Neural Networks and Word/Phone Embeddings

Automatic prediction of articulatory movements from speech or text can be beneficial for many applications such as speech recognition and synthesis. A recent approach has reported state-of-the-art performance in speech-to-articulatory prediction using feed forward neural networks. In this paper, we investigate the feasibility of using bidirectional long short-term memory based recurrent neural networks (BLSTM-RNNs) in articulatory movement prediction because they have long-context trajectory modeling ability. We show on the MNGU0 dataset that BLSTM-RNN apparently outperforms feed forward networks and pushes the state-of-the-art RMSE from 0.885 mm to 0.565 mm. On the other hand, predicting articulatory information from text heavily relies on handcrafted linguistic and prosodic features, e.g., POS and TOBI labels. In this paper, we propose to use word and phone embeddings to substitute these manual features. Word/phone embedding features are automatically learned from unlabeled text data by a neural network language model. We show that word and phone embeddings can achieve comparable performance without using POS and TOBI features. More promisingly, combining the conventional full feature set with phone embedding, the lowest RMSE is achieved.