从众包半结构化语音记录中分类自闭症：一种机器学习方法
Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach
摘要：
自闭症谱系障碍（ASD）是一种神经发育障碍，导致行为，社会发展和交流模式的改变。在过去的几年里，自闭症的患病率增加了两倍，现在每54名儿童中就有1名受到影响。鉴于传统的诊断是一个漫长的劳动密集型过程，开发自动筛查自闭症的系统受到了极大的关注。韵律异常是自闭症最明显的迹象之一，受影响的儿童表现出言语特质，包括回声言语症，单调的语调，非典型的音高和不规则的语言重音模式。在这项工作中，我们提出了一套机器学习方法来检测自闭症的自我记录的语音音频捕获自闭症和神经型（NT）儿童在家庭环境中。我们考虑了三种方法来检测儿童语音中的自闭症：第一，在提取的音频特征（包括梅尔频率倒谱系数）上训练的随机森林;第二，在频谱图上训练的卷积神经网络（CNN）;第三，微调的wav 2 vec 2.0-最先进的基于transformer的ASR模型。我们训练我们的分类器对我们的新数据集的手机记录的儿童语音音频策划从斯坦福大学的猜猜是什么？手机游戏，一个应用程序，旨在众包视频自闭症和神经正常的儿童在一个自然的家庭环境。在将儿童音频分类为ASD或NT时，随机森林分类器的准确率达到70%，微调的wav 2 vec 2.0模型达到77%的准确率，而CNN达到79%的准确率。我们的模型能够预测自闭症状态，当训练不同选择的家庭音频片段时，录音质量不一致，这可能更适用于现实世界的条件。这些结果表明，机器学习方法有望在没有专门设备的情况下从语音中自动检测自闭症。
英文摘要：
Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results in altered behavior, social development, and communication patterns. In past years, autism prevalence has tripled, with 1 in 54 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process, significant attention has been given to developing systems that automatically screen for autism. Prosody abnormalities are among the clearest signs of autism, with affected children displaying speech idiosyncrasies including echolalia, monotonous intonation, atypical pitch, and irregular linguistic stress patterns. In this work, we present a suite of machine learning approaches to detect autism in self-recorded speech audio captured from autistic and neurotypical (NT) children in home environments. We consider three methods to detect autism in child speech: first, Random Forests trained on extracted audio features (including Mel-frequency cepstral coefficients); second, convolutional neural networks (CNNs) trained on spectrograms; and third, fine-tuned wav2vec 2.0--a state-of-the-art Transformer-based ASR model. We train our classifiers on our novel dataset of cellphone-recorded child speech audio curated from Stanford's Guess What? mobile game, an app designed to crowdsource videos of autistic and neurotypical children in a natural home environment. The Random Forest classifier achieves 70% accuracy, the fine-tuned wav2vec 2.0 model achieves 77% accuracy, and the CNN achieves 79% accuracy when classifying children's audio as either ASD or NT. Our models were able to predict autism status when training on a varied selection of home audio clips with inconsistent recording quality, which may be more generalizable to real world conditions. These results demonstrate that machine learning methods offer promise in detecting autism automatically from speech without specialized equipment.
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