A bi-directional LSTM approach for polyphone disambiguation in Mandarin Chinese

Changhao Shan, Lei Xie, Kaisheng Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

Polyphone disambiguation in Mandarin Chinese aims to pick up the correct pronunciation from several candidates for a polyphonic character. It serves as an essential component in human language technologies such as text-to-speech synthesis. Since the pronunciation for most polyphonic characters can be easily decided from their contexts in the text, in this paper, we address the polyphone disambiguation problem as a sequential labeling task. Specifically, we propose to use bidirectional long short-term memory (BLSTM) neural network to encode both the past and future observations on the character sequence as its inputs and predict the pronunciations. We also empirically study the impacts of (1) modeling different length of contexts, (2) the number of BLSTM layers and (3) the granularity of part-o-speech (POS) tags as features. Our results show that using a deep BLSTM is able to achieve state-of-the-art performance in polyphone disambiguation.

Original languageEnglish
Title of host publicationProceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
EditorsHsin-Min Wang, Qingzhi Hou, Yuan Wei, Tan Lee, Jianguo Wei, Lei Xie, Hui Feng, Jianwu Dang, Jianwu Dang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042937
DOIs
StatePublished - 2 May 2017
Event10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 - Tianjin, China
Duration: 17 Oct 201620 Oct 2016

Publication series

NameProceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

Conference

Conference10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
Country/TerritoryChina
CityTianjin
Period17/10/1620/10/16

Keywords

  • Bi-directional LSTM
  • Grapheme-to-phoneme conversion
  • Polyphone disambiguation
  • Sequence tagging
  • Text-to-Speech

Fingerprint

Dive into the research topics of 'A bi-directional LSTM approach for polyphone disambiguation in Mandarin Chinese'. Together they form a unique fingerprint.

Cite this