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首页> 外文会议>Workshop on computational linguistics and clinical psychology 2014 >Comparison of different feature sets for identification of variants in progressive aphasia
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Comparison of different feature sets for identification of variants in progressive aphasia

机译：比较不同特征集以识别进行性失语症的变异

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We use computational techniques to extract a large number of different features from the narrative speech of individuals with primary progressive aphasia (PPA). We examine several different types of features, including part-of-speech, complexity, context-free grammar, fluency, psy-cholinguistic, vocabulary richness, and acoustic, and discuss the circumstances under which they can be extracted. We consider the task of training a machine learning classifier to determine whether a participant is a control, or has the fluent or nonfluent variant of PPA. We first evaluate the individual feature sets on their classification accuracy, then perform an ablation study to determine the optimal combination of feature sets. Finally, we rank the features in four practical scenarios: given audio data only, given unsegmented transcripts only, given segmented transcripts only, and given both audio and segmented transcripts. We find that psycholinguis-tic features are highly discriminative in most cases, and that acoustic, context-free grammar, and part-of-speech features can also be important in some circumstances.
机译：我们使用计算技术从具有初级进行性失语症（PPA）的个体的叙述性语音中提取大量不同的特征。我们研究了几种不同类型的功能，包括词性，复杂性，与上下文无关的语法，流利性，心理语言学，词汇丰富性和听觉性，并讨论了在何种情况下可以提取它们。我们考虑训练机器学习分类器的任务，以确定参与者是控制者，还是流利的或不流利的PPA变体。我们首先评估各个特征集的分类精度，然后进行消融研究以确定特征集的最佳组合。最后，我们在四个实际场景中对功能进行排名：仅给定音频数据，仅给定未分段的成绩单，仅给定分段的成绩单，以及给定音频和分段的成绩单。我们发现，在大多数情况下，心理语言特征具有很高的判别力，在某些情况下，声学，无上下文语法和词性特征也很重要。

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