An Investigation of Convolution Attention Based Models for Multilingual Speech Synthesis of Indian Languages

被引：7

作者：

Baljekar, Pallavi ^{[1
]}

Rallabandi, SaiKrishna ^{[1
]}

Black, Alan W. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

来源：

19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES | 2018年

关键词：

End-to-end Synthesis; Convolutional Model; Neural Networks; Indic Languages;

D O I：

10.21437/Interspeech.2018-1869

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we investigate multi-speaker, multi-lingual speech synthesis for 4 Indic languages (Hindi, Marathi, Gujarathi, Bengali) as well as English in a fully convolutional attention based model. We show how factored embeddings can allow cross lingual transfer, and investigate methods to adapt the model in a low resource scenario for the case of Marathi and Gujarati. We also show results on how effectively the model scales to a new language and how much data is required to train the system on a new language.

引用

页码：2474 / 2478

页数：5