论文标题
重新发现斯拉夫连续体的表征来自口语识别的神经模型
Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification
论文作者
论文摘要
深层神经网络已用于各种口头语言识别任务，包括按照定义的多语言任务，例如口语识别。在本文中，我们提出了一种神经模型，用于语音信号中的斯拉夫语言识别，并分析其新兴表现，以调查它们是否反映了语言相关性和/或非语言学家对语言相似性的看法。尽管我们的分析表明，语言表示空间确实在很大程度上捕获了语言相关性，但我们发现我们研究中语言之间的感知性混乱是语言表示相似性的最佳预测指标。
Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness and/or non-linguists' perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability between languages in our study to be the best predictor of the language representation similarity.
