论文标题
在L2英语语音中，基于深度节段的语音后验发现非类别的发现
Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speech
论文作者
论文摘要
第二语言（L2）语音通常用本机，电话类别标记。但是，在许多情况下，很难决定使用L2段所属的分类手机。这些细分被视为非类别。大多数现有的错误发音检测和诊断方法（MDD）仅与分类错误有关，即手机类别被另一个错误插入，删除或替代。但是，不考虑非类别错误。为了建模这些非类别错误，这项工作旨在探索非类别模式以扩展分类手机集。我们应用语音段分类器来生成分段语音后验（SPPG）来表示电话段级信息。然后，我们通过寻找多个峰的SPPG来探索非类别。与基线系统相比，该方法探讨了更多非类别模式，并且感知实验结果表明，在两种不同的措施下，探索的非类别更准确，混淆度增加了7.3％和7.5％。最后，我们初步分析了这些非类别背后的原因。
Second language (L2) speech is often labeled with the native, phone categories. However, in many cases, it is difficult to decide on a categorical phone that an L2 segment belongs to. These segments are regarded as non-categories. Most existing approaches for Mispronunciation Detection and Diagnosis (MDD) are only concerned with categorical errors, i.e. a phone category is inserted, deleted or substituted by another. However, non-categorical errors are not considered. To model these non-categorical errors, this work aims at exploring non-categorical patterns to extend the categorical phone set. We apply a phonetic segment classifier to generate segmental phonetic posterior-grams (SPPGs) to represent phone segment-level information. And then we explore the non-categories by looking for the SPPGs with more than one peak. Compared with the baseline system, this approach explores more non-categorical patterns, and also perceptual experimental results show that the explored non-categories are more accurate with increased confusion degree by 7.3% and 7.5% under two different measures. Finally, we preliminarily analyze the reason behind those non-categories.
