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基于混合模型状态修正算法的非母语语音识别
  • 期刊名称：数字通信
  • 时间：0
  • 页码：33-37
  • 语言：中文
  • 分类：TP391.4[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] TS190.642[轻工技术与工程—纺织化学与染整工程;轻工技术与工程—纺织科学与工程]
  • 作者机构：[1]中国科学院声学研究所,北京100080
  • 相关基金：基金项目：国家高技术研究发展计划（863计划,2006AA010102,2006AA01Z195）;国家重点基础研究发展窥划项目计划（973计划,2004CB318106）;国家自然科学基金（No.10574140,60535030）
  • 相关项目：口吃语音的自动评估和矫正方法研究
作者： 张晴晴|颜永红|潘接林|
关键词： 非母语语音识别, 模型修正, 混合模型, nonnative speech recognition, model modification, mixed model
中文摘要：

非母语语音识别的性能较低，对于刚开始学习目标语言的说话人或者口音很重的说话人而言，性能下降更为明显。本文提出一种新型的双语模型修正算法用于提高非母语语音的识别性能。在该算法中，基线声学模型的每个状态都将被代表说话人母语特点的辅助模型状态所修正。文章给出了状态修正准则以及不同候选修正状态数下的性能比较。相比已用非母语训练数据自适应以后的基线声学模型，通过双语模型修正的声学模型在保证识别实时率的前提下，短语错误率相对下降了11．7％。

英文摘要：

The performance of automatic speech recognition decreases drastically for nonnative speakers, especially those who are just beginning to learn foreign language or who have heavy accents. A novel bilingual model modification approach is presented to improve nonnative speech recognition accuracy. Each state of baseline nonnative acoustic model is modified with several candidate states from the auxiliary acoustic model, which is trained by speakers＇ mother tongue. State mapping criterion and n-best candidates are investigated. Using this bilingual model modification approach, compared to the nonnative acoustic model which has already been well trained by adaptation technique MAP, the phrase error rate further is reduced by 11.7% relatively, while only a small relative increase on real time factor occurs.

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