AURORA-2J: An Evaluation Framework for Japanese Noisy Speech Recognition
AURORA-2J: An Evaluation Framework for Japanese Noisy Speech Recognition
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DOI：
10.1093/ietisy/e88-d.3.535
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发表时间：
2005-03
期刊：
影响因子：
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通讯作者：
Satoshi Nakamura;K. Takeda;Kazumasa Yamamoto;Takeshi Yamada;S. Kuroiwa;N. Kitaoka;T. Nishiura;A. Sasou;M. Mizumachi;C. Miyajima;Masakiyo Fujimoto;Toshiki Endo
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文献类型：
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作者：
Satoshi Nakamura;K. Takeda;Kazumasa Yamamoto;Takeshi Yamada;S. Kuroiwa;N. Kitaoka;T. Nishiura;A. Sasou;M. Mizumachi;C. Miyajima;Masakiyo Fujimoto;Toshiki Endo
本文介绍了一个日语带噪语音识别评价框架Aurora-2J。语音识别系统仍然需要改进，以对噪声环境具有健壮性，但这种改进需要开发标准的评估语料库和评估技术。最近，Aurora 2、3和4语料库及其评估场景对噪声语音识别研究产生了重大影响。Aurora-2J是一个日语连接数字语料库，它的评估脚本是在欧洲电信标准协会(ETSI)Aurora小组的帮助下以与Aurora 2相同的方式设计的。本文描述了数据收集、基线脚本及其基线性能。我们还提出了一种新的性能分析方法，该方法考虑了说话人之间识别性能的差异。该方法基于每个说话人的单词准确率，揭示了个体在识别性能上的差异程度。我们还建议对应用于原始HTK基线系统的修改进行分类，这有助于比较这些系统，并识别在同一类别中改进性能最好的技术。
This paper introduces an evaluation framework for Japanese noisy speech recognition named AURORA-2J. Speech recognition systems must still be improved to be robust to noisy environments, but this improvement requires development of the standard evaluation corpus and assessment technologies. Recently, the Aurora 2, 3 and 4 corpora and their evaluation scenarios have had significant impact on noisy speech recognition research. The AURORA-2J is a Japanese connected digits corpus and its evaluation scripts are designed in the same way as Aurora 2 with the help of European Telecommunications Standards Institute (ETSI) AURORA group. This paper describes the data collection, baseline scripts, and its baseline performance. We also propose a new performance analysis method that considers differences in recognition performance among speakers. This method is based on the word accuracy per speaker, revealing the degree of the individual difference of the recognition performance. We also propose categorization of modifications, applied to the original HTK baseline system, which helps in comparing the systems and in recognizing technologies that improve the performance best within the same category.