In this paper, a novel Hybrid Deep Ensemble (HDE) is proposed for automatic speech disfluency classification on a sparse speech dataset. Categorizations of speech disfluencies for diagnosis of speech disorders have so long relied on sophisticated deep learning models. Such a task can be accomplished by a straightforward approach with high accuracy by the proposed model which is an optimal combination of diverse machine learning and deep learning algorithms in a hierarchical arrangement which includes a deep autoencoder that yields the compressed latent features. The proposed model has shown considerable improvement in downgrading processing time overcoming the issues of cumbersome hyper-parameter tuning and huge data demand of the deep learning algorithms with high classification accuracy. Experimental results show that the proposed Hybrid Deep Ensemble has superior performance compared to the individual base learners, and the deep neural network as well. The proposed model and the baseline models were evaluated in terms of Cohen’s kappa coefficient, Hamming loss, Jaccard score, F-score and classification accuracy.

在本文中，提出了一种新颖的混合深度集成（HDE），用于在稀疏语音数据集上自动进行语音不满分类。长期以来，用于诊断语言障碍的语音障碍的分类一直依赖于复杂的深度学习模型。可以通过提出的模型通过具有高精度的直接方法来完成这样的任务，该模型是分层排列中各种机器学习和深度学习算法的最佳组合，该分层排列包括产生压缩的潜在特征的深度自动编码器。提出的模型在降级处理时间方面显示出显着的改进，克服了麻烦的超参数调整和具有高分类精度的深度学习算法的巨大数据需求的问题。实验结果表明，与单独的基础学习者和深度神经网络相比，提出的混合深度集成具有更好的性能。根据Cohen的kappa系数，汉明损失，Jaccard得分，F得分和分类准确性，对提出的模型和基线模型进行了评估。