# Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features

arXiv: https://arxiv.org/abs/1511.00360 · HTML: https://mlsanity.com/paper/1511.00360/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech](https://mlsanity.com/paper/2302.13652/) (2023)
- [A Character-level Span-based Model for Mandarin Prosodic Structure Prediction](https://mlsanity.com/paper/2203.16922/) (2022)
- [Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information](https://mlsanity.com/paper/2308.16577/) (2023)

## Abstract (arXiv)

Prosody affects the naturalness and intelligibility of speech. However, automatic prosody prediction from text for Chinese speech synthesis is still a great challenge and the traditional conditional random fields (CRF) based method always heavily relies on feature engineering. In this paper, we propose to use neural networks to predict prosodic boundary labels directly from Chinese characters without any feature engineering. Experimental results show that stacking feed-forward and bidirectional long short-term memory (BLSTM) recurrent network layers achieves superior performance over the CRF-based method. The embedding features learned from raw text further enhance the performance.