Many children with speech sound disorders cannot pronounce the sibilant consonants correctly. We have developed a serious game, which is controlled by the children's voices in real time, with the purpose of helping children on practicing the production of European Portuguese (EP) sibilant consonants. For this, the game uses a sibilant consonant classifier. Since the game does not require any type of adult supervision, children can practice producing these sounds more often, which may lead to faster improvements of their speech. Recently, the use of deep neural networks has given considerable improvements in the classification of a variety of use cases, from image classification to speech and language processing. Here, we propose to use deep convolutional neural networks to classify sibilant phonemes of EP in our serious game for speech and language therapy. We compared the performance of several different artificial neural networks that used Mel frequency cepstral coefficients or log Mel filterbanks. Our best deep learning model achieves classification scores of 95.48% using a 2D convolutional model with log Mel filterbanks as input features. Such results are then further improved for specific classes with simple binary classifiers.

中文翻译：

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多级和单级神经网络对辅音的分类比较

许多患有语音障碍的儿童无法正确发音沉默的辅音。我们开发了一款严肃的游戏，该游戏由孩子们的声音实时控制，目的是帮助孩子们练习制作欧洲葡萄牙语（EP）稳定辅音。为此，游戏使用了简单的辅音分类器。由于游戏不需要任何类型的成人监督，因此儿童可以练习更频繁地产生这些声音，从而可以更快地改善其语音。最近，深度神经网络的使用在从图像分类到语音和语言处理的各种用例的分类中都取得了很大的进步。这里，我们建议在我们严肃的语音和语言治疗游戏中，使用深度卷积神经网络对EP的简单音素进行分类。我们比较了使用梅尔频率倒谱系数或对数梅尔滤波器组的几种不同人工神经网络的性能。我们最好的深度学习模型使用对数梅尔滤波器组作为输入特征的2D卷积模型获得95.48％的分类分数。然后，使用简单的二进制分类器对特定类别的此类结果进行进一步改进。