In this paper, the Deep Long-short term memory Autoencoder (DLAE), a regularized deep learning model, is proposed for the automatic severity assessment of phonological deviations which are crucial stuttering markers in children. This automatic noninvasive severity assessment plays a paramount role in prevenient diagnosis, progress inference, and post-care for the patients with specific speech disorder. The proposed model is an implementation of a multi-layered Autoencoder in the Encoder–Decoder architecture of the Long-Short Term Memory (LSTM) model with hierarchically appended hidden layers and hidden units. The DLAE has definite advantage over the baseline Autoencoders. During the training phase, the proposed DLAE reconstructs the phonological features in an unsupervised fashion and the latent bottleneck features are extracted from the Encoder. The trained and regularized DLAE model with drop out is then used to predict the severity of the phonological deviation with high precision and classification accuracy compared to the baseline models.

中文翻译：

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用于语音偏差评估的正则化深度 LSTM 自动编码器

在本文中，提出了深度长短期记忆自动编码器 (DLAE)，一种正则化的深度学习模型，用于自动评估语音偏差的严重程度，语音偏差是儿童口吃的关键标志。这种自动无创严重程度评估在针对特定语言障碍患者的早期诊断、进展推断和后期护理中发挥着至关重要的作用。所提出的模型是在具有分层附加隐藏层和隐藏单元的长短期记忆 (LSTM) 模型的编码器-解码器架构中实现多层自动编码器。DLAE 比基线自动编码器具有明显的优势。在训练阶段，所提出的 DLAE 以无监督的方式重建语音特征，并从编码器中提取潜在的瓶颈特征。