Leveraging translations for speech transcription in low-resource settings

被引:0
|
作者
Anastasopoulos, Antonios [1 ]
Chiang, David [1 ]
机构
[1] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46556 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
基金
美国国家科学基金会;
关键词
neural multi-source models; speech transcription; endangered languages;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently proposed data collection frameworks for endangered language documentation aim not only to collect speech in the language of interest, but also to collect translations into a high resource language that will render the collected resource interpretable. We focus on this scenario and explore whether we can improve transcription quality under these extremely low resource settings with the assistance of text translations. We present a neural multi-source model and evaluate several variations of it on three low-resource datasets. We find that our multi-source model with shared attention outperforms the baselines, reducing transcription character error rate by up to 12.3%.
引用
收藏
页码:1279 / 1283
页数:5
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