Phonologically Aware BiLSTM Model for Mongolian Phrase Break Prediction with Attention Mechanism

Abstract

Phrase break prediction is the first and most important component in increasing naturalness and intelligibility of text-to-speech (TTS) systems. Most works rely on language specific resources, large annotated corpus and feature engineering to perform well. However, phrase break prediction from text for Mongolian speech synthesis is still a great challenge because the data sparse problem due to the scarcity of resources. In this paper, we introduce a Bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanism which uses the position-based enhanced phonological representations, word embeddings and character embeddings to achieve state of the art performance. The position-based enhanced phonological representations, derived from a separately BiLSTM model, are comprised of phoneme and syllable embeddings which take along position information. By using an attention mechanism, the model is able to dynamically decide how much information to use from a word or phonological component. To handle Out-of-Vocabulary (OOV) problem, we incorporated word, phonological and character embeddings together as inputs to the model. Experimental results show the proposed method significantly outperforms the systems which only used the word embeddings by successfully leveraging position-based phonologically information and attention mechanism.