Dysarthria is a motor speech impairment, often characterized by slow and slurred speech that is generally incomprehensible by human listeners. An understanding of the intelligibility level of the patient's dysarthric speech can provide an insight into the progression/status of the underlying cause and is essential for planning therapy. Automatic assessment of dysarthric speech intelligibility can be of immense value and serve to assist speech language pathologists in diagnosis and therapy. However, this is a non-trivial problem due to the high intra and inter speaker variability in dysarthric speech. In this article we propose a machine learning-based method to automatically classify dysarthric speech into intelligible (I) and non-intelligible (NI) using Bidirectional Long-Short Term Memory (BLSTM) Networks. We explored balancing of training data to represent both the classes almost equally and its implications on the binary classification. Additionally, we present a mechanism to use the available pre-trained acoustic models for transfer-learning. It was observed that the transfer learning method was able to handle channel noise. This technique provided significant improvement of roughly 6% as compared to traditional machine learning method.

中文翻译：

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使用 BLSTM 自动评估句子级构音障碍

构音障碍是一种运动性言语障碍，通常以人类听众通常无法理解的缓慢和含糊的言语为特征。了解患者构音障碍语音的可懂度水平可以深入了解潜在原因的进展/状态，并且对于规划治疗至关重要。构音障碍语音清晰度的自动评估具有巨大的价值，有助于语言病理学家进行诊断和治疗。然而，由于构音障碍语音的说话者内部和说话者间的高可变性，这是一个不平凡的问题。在本文中，我们提出了一种基于机器学习的方法，使用双向长短期记忆 (BLSTM) 网络将构音障碍语音自动分类为可理解 (I) 和不可理解 (NI)。我们探索了训练数据的平衡以几乎平等地表示两个类及其对二元分类的影响。此外，我们提出了一种使用可用的预训练声学模型进行迁移学习的机制。据观察，转移学习方法能够处理信道噪声。与传统的机器学习方法相比，该技术提供了大约 6% 的显着改进。