Recently, spectro-temporal representation of speech has been used in many fields of speech processing. Owing to this, we explore the use of spectro-temporal representation for speech intelligibility assessment especially for dysarthric speech. In this work, we investigate the use of spectro-temporal representations to evaluate intelligibility levels using artificial neural network (ANN) and convolutional neural network (CNN). Standard American English dysarthric databases namely Universal Access and TORGO are used for evaluation. Performance of CNN classifier is superior to ANN as it is an advanced classifier. Further, use of Time-Frequency CNN configuration proved to capture spectro-temporal variations together resulting in an improved performance compared to either Time-CNN or Frequency-CNN configurations which capture either temporal or spectral variations respectively.

中文翻译：

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用于构音障碍清晰度评估的语音的时空表征

最近，语音的谱时间表示已被用于语音处理的许多领域。因此，我们探索了使用光谱时间表示进行语音可懂度评估，尤其是构音障碍语音。在这项工作中，我们使用人工神经网络 (ANN) 和卷积神经网络 (CNN) 研究使用光谱时间表示来评估可懂度水平。标准美式英语构音障碍数据库即 Universal Access 和 TORGO 用于评估。CNN 分类器的性能优于 ANN，因为它是一种高级分类器。更多，