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首页> 外文期刊>Journal of VLSI signal processing systems for signal, image, and video technology >An End-to-End Approach to Automatic Speech Assessment for Cantonese-speaking People with Aphasia
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An End-to-End Approach to Automatic Speech Assessment for Cantonese-speaking People with Aphasia

机译：对粤语讲话的粤语人的自动演讲评估的端到端方法

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摘要

Conventional automatic assessment of pathological speech usually follows two main steps: (1) extraction of pathology-specific features; (2) classification or regression on extracted features. Given the great variety of speech and language disorders, feature design is never a straightforward task, and yet it is most crucial to the performance of assessment. This paper presents an end-to-end approach to automatic speech assessment for Cantonese-speaking People With Aphasia (PWA). The assessment is formulated as a binary classification task to discriminate PWA with high scores of subjective assessment from those with low scores. The 2-layer Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) models are applied to realize the end-to-end mapping from basic speech features to the classification outcome. The pathology-specific features used for assessment are learned implicitly by the neural network model. The Class Activation Mapping (CAM) method is utilized to visualize how the learned features contribute to the assessment result. Experimental results show that the end-to-end approach can achieve comparable performance to the conventional two-step approach in the classification task, and the CNN model is able to learn impairment-related features that are similar to the hand-crafted features. The experimental results also indicate that CNN model performs better than 2-layer GRU model in this specific task.
机译：传统的病理语音自动评估通常遵循两个主要步骤：（1）提取病理学特征; （2）提取特征对分类或回归。鉴于各种各样的言语和语言障碍，功能设计绝不是一项直接的任务，但它对于评估性能至关重要。本文介绍了对粤语人物（PWA）的粤语人的自动演讲评估的端到端方法。评估作为二进制分类任务制定，以区分PWA，以极高分数的高度评估。应用2层门控复发单元（GRU）和卷积神经网络（CNN）模型来实现从基本语音特征到分类结果的端到端映射。用于评估的病理学特定特征由神经网络模型隐含地学习。类激活映射（CAM）方法用于可视化学习功能如何促进评估结果。实验结果表明，端到端方法可以在分类任务中实现与传统的两步方法的相当性能，并且CNN模型能够学习与手工制作功能类似的损伤相关的特征。实验结果还表明CNN模型在该特定任务中执行了比2层GRU模型更好。

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