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An automated assessment framework for atypical prosody and stereotyped idiosyncratic phrases related to autism spectrum disorder

机译：与自闭症频谱障碍有关的非典型韵律和陈规定型特殊短语的自动评估框架

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Autism Spectrum Disorder (ASD), a neurodevelopmental disability, has become one of the high incidence diseases among children. Studies indicate that early diagnosis and intervention treatments help to achieve positive longitudinal outcomes. In this paper, we focus on the speech and language abnormalities of young children with ASD and present an automated assessment framework in quantifying atypical prosody and stereotyped idiosyncratic phrases related to ASD. For detecting atypical prosody from speech, we propose both the hand-crafted feature based method as well as the end-to-end deep learning framework. First, we use the Open-SMILE toolkit to extract utterance level high dimensional acoustic features followed by a support vector machine (SVM) backend as the conventional baseline. Second, we propose several end-to-end deep neural network setups and configurations to model the atypical prosody label directly from the constant Q transform spectrogram of speech. Third, we apply cross-validation on the training data to perform segments selection and enhance the subject level classification performance. Fourth, we fuse the deep learning based methods with the conventional baseline at the score level to further enhance the overall system performance. For detecting the stereotyped idiosyncratic usage of words or phrases from speech transcripts, we adopt language model, dependency treebank and Term Frequency-Inverse Document Frequency (TF-IDF) in addition to Linguistic Inquiry and Word Count software (LIWC) methods to extract a set of text features followed by a standard SVM backend. We collect a database of spontaneous Mandarin speech recorded during the Autism Diagnostic Observation Schedule (ADOS) Module 2 and Module 3 sessions. The Module 2 part consists of 118 children while the Module 3 part includes 71 children. Experimental results on this database show that our proposed methods can effectively predict the atypical prosody and stereotyped idiosyncratic phrases codes for young children with the risk of ASD. On the two categories classification task, the unweighted accuracy of the aforementioned two tasks are 88.1% and 77.8%, respectively. (C) 2018 Published by Elsevier Ltd.
机译：自闭症谱系障碍（ASD），神经发育障碍，已成为儿童的高发病疾病之一。研究表明，早期诊断和干预治疗有助于达到正纵向结果。在这篇论文中，我们专注于亚斯达特幼儿幼儿的语音和语言异常，并在量化与ASD相关的非典型韵律和陈规定型特殊的短语中的自动评估框架。从言语中检测到非典型硕士学位，我们提出了基于手工制作的特征的方法以及端到端的深度学习框架。首先，我们使用开放式笑脸工具包来提取话语级高维声学特征，然后是支持向量机（SVM）后端作为传统基线。其次，我们提出了几个端到端的深神经网络设置和配置，以直接从语音的常数Q变换谱图模拟非典型韵律标签。第三，我们在训练数据上应用交叉验证来执行段选择并增强主题级别分类性能。第四，我们融合了基于深度学习的方法，在分数水平处具有传统基线，以进一步提高整体系统性能。除了语音转录物中，我们采用语言模型，依赖树库和术语频率 - 逆文档频率（TF-IDF）除了语言查询和单词数量软件（LIWC）方法中提取一个集中的语言模型，依赖性树译文本功能后跟标准SVM后端。我们收集自闭症诊断观察计划（ADOS）模块2和模块3会话期间录制的自发普通话语音数据库。模块2部分由118名儿童组成，而模块3部分包括71个孩子。该数据库的实验结果表明，我们的建议方法可以有效地预测具有亚本大风险的幼儿的非典型韵律和陈规定型特殊的特质。在两类分类任务中，上述两项任务的未加权准确性分别为88.1％和77.8％。 （c）2018年由elestvier有限公司发布

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