Computer Science ›› 2026, Vol. 53 ›› Issue (10): 142-155.doi: 10.11896/jsjkx.250800113

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Review on Deep Learning-based Cross-lingual Speech Synthesis Technology

ZHOU Zhitong, ZHOU Ruohua, GUO Ziye, ZHAO Mingyuan   

  1. School of Intelligent Science and Technology,Beijing University of Civil Engineering and Architecture,Beijing 102616, China
  • Received:2025-08-28 Revised:2026-02-24 Published:2026-10-13
  • About author:ZHOU Zhitong,born in 2002,postgra-duate,is a member of CCF(No.Z6464G).Her main research interests include speech synthesis and cross-lingual speech synthesis.
    ZHOU Ruohua,born in 1972,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.c2086m).His main research interests include speaker verification,machine learning,and speech & music signal processing.
  • Supported by:
    National Natural Science Foundation of China(11590774,62371032).

Abstract: With the continuous advancement of speech synthesis technology,cross-lingual text-to-speech has made significant progress.Despite notable progress,current studies still face gaps in cross-lingual consistency in pronunciation and prosody,speaker timbre preservation,and unified,comparable evaluation.This paper provides a comprehensive review of recent developments in the field,covering model architectures,key cross-lingual techniques,datasets,and evaluation metrics.Firstly,it introduces the main types and limitations of cross-lingual TTS.Then,it analyzes universal architectures,low-resource synthesis,feature disentanglement,zero-shot learning,adversarial training,emotional transfer,and large-scale pre-trained models.It also summarizes and compares commonly used datasets and evaluation methods.Finally,it discusses future directions for cross-lingual TTS.

Key words: Cross-lingual speech synthesis, Deep learning, Generative neural networks, Multilingual feature mapping

CLC Number: 

  • TP391.4
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