Zero-Shot Pronunciation Lexicons for Cross-Language Acoustic Model Transfer

Matthew Wiesner, Oliver Adams, David Yarowsky, J. Trmal, S. Khudanpur
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引用次数: 6

Abstract

Existing acoustic models can be transferred to any language with a pronunciation lexicon (lexicon) that uses the same set of sub-word units as in training. Unfortunately such lexicons are not readily available in many low-resource languages. We bypass this requirement and create lexicons by training a grapheme-to-phoneme (G2P) transducer on a subset of words from other languages for which pronunciations are available. The subset of words is selected based on how representative it is of target language text. We find that cross-language acoustic model transfer using our selection strategy outperforms selection based on language similarity, and results in ASR performance approaching that of hand-crafted rule based lexicons in the majority of cases.
跨语言声学模型迁移的零发音词汇
现有的声学模型可以转移到任何语言的发音词典(词典),使用与训练中相同的一组子词单位。不幸的是,在许多资源匮乏的语言中,这样的词汇并不容易获得。我们绕过了这一要求,并通过在其他语言中可用发音的单词子集上训练一个字形到音素(G2P)换能器来创建词汇。单词子集的选择是基于它对目标语言文本的代表性。我们发现,使用我们的选择策略的跨语言声学模型迁移优于基于语言相似度的选择,并且在大多数情况下,其ASR性能接近基于手工规则的词汇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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