Name
Title | ||
|---|---|---|
Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features |
Abstract | ||
|---|---|---|
This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation. The system is composed of a bidirectional recurrent neural network component acting as a sentence encoder to accumulate the context correlations, followed by a prediction network that maps the polyphonic character embeddings along with the conditions to corresponding pronunciations. We obtain the word-level condition from a pre-trained word-to-vector lookup table. One goal of polyphone disambiguation is to address the homograph problem existing in the front-end processing of Mandarin Chinese text-to-speech system. Our system achieves an accuracy of 94.69\% on a publicly available polyphonic character dataset. To further validate our choices on the conditional feature, we investigate polyphone disambiguation systems with multi-level conditions respectively. The experimental results show that both the sentence-level and the word-level conditional embedding features are able to attain good performance for Mandarin Chinese polyphone disambiguation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.21437/Interspeech.2019-1235 | INTERSPEECH |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zexin Cai | 1 | 2 | 2.75 |
| Yaogen Yang | 2 | 0 | 0.34 |
| Chuxiong Zhang | 3 | 0 | 0.34 |
| Xiaoyi Qin | 4 | 2 | 2.03 |
| Ming Li | 5 | 3 | 2.05 |