基于有限训练样本的自动词汇学习方法

N. Goel, Samuel Thomas, Mohit Agarwal, Pinar Akyazi, L. Burget, Kai Feng, Arnab Ghoshal, O. Glembek, M. Karafiát, Daniel Povey, A. Rastrow, R. Rose, Petr Schwarz
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引用次数: 30

摘要

在书面形式不完全是语音的语言中,为语音识别系统准备词典可能是一项重要的工作。另一方面,在书面形式非常注重语音的语言中,一些常用词经常发错音。在本文中,我们使用词汇学习技术的组合来探索当只有一个小的词汇可用于引导时,是否可以学习一个词汇。我们发现,对于像西班牙语这样的语音语言,它可能比一般规则或手工发音做得更好。对于像英语这样更复杂的语言,我们发现它仍然是可能的,但准确性会有所下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approaches to automatic lexicon learning with limited training examples
Preparation of a lexicon for speech recognition systems can be a significant effort in languages where the written form is not exactly phonetic. On the other hand, in languages where the written form is quite phonetic, some common words are often mispronounced. In this paper, we use a combination of lexicon learning techniques to explore whether a lexicon can be learned when only a small lexicon is available for boot-strapping. We discover that for a phonetic language such as Spanish, it is possible to do that better than what is possible from generic rules or hand-crafted pronunciations. For a more complex language such as English, we find that it is still possible but with some loss of accuracy.
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