基于多语言训练声学模型的零资源语言口语词检测

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) Pub Date : 2020-10-13 DOI:10.1109/GCCE50665.2020.9291761

Satoru Mizuochi, Yuya Chiba, Takashi Nose, A. Ito

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引用次数: 0

摘要

本文研究了一种基于富资源语言的零资源语言口语术语检测方法。该方法结合从多种语言的音位分类器中提取的音位后图(PPGs)，并基于动态时间翘曲检测查询词。结果表明，该方法在零资源语言下的检测性能优于使用单一语言的ppg检测方法。

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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Spoken Term Detection Based on Acoustic Models Trained in Multiple Languages for Zero-Resource Language

In this paper, we study a spoken term detection method for zero-resource languages by using rich-resource languages. The examined method combines phonemic posteriorgrams (PPGs) extracted from phonemic classifiers of multiple languages and detects a query word based on dynamic time warping. As a result, the method showed better detection performance in a zero-resource language compared with the method using PPGs of a single language.

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来源期刊

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)

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