论文标题
基于深度学习的辅音元音过渡模型，以进行客观评估
Consonant-Vowel Transition Models Based on Deep Learning for Objective Evaluation of Articulation
论文作者
论文摘要
辅音元音（CV）过渡区域的光谱动力学被认为提供了与关节相关的强大提示。在这项工作中，我们通过分析围绕元音围绕元音的CV过渡，提出了一种被称为客观关节措施（OAM）的客观度量。 OAM是根据预先训练的卷积神经网络的后期来得出的，以使用CV区域作为输入进行分类。我们证明，在多种情况下，OAM与感知度量相关，包括（a）成人违反术语，（b）患有唇lip/paple的儿童的演讲，以及（c）来自本地普通话和西班牙语者的重音英语演讲数据库。
Spectro-temporal dynamics of consonant-vowel (CV) transition regions are considered to provide robust cues related to articulation. In this work, we propose an objective measure of precise articulation, dubbed the objective articulation measure (OAM), by analyzing the CV transitions segmented around vowel onsets. The OAM is derived based on the posteriors of a convolutional neural network pre-trained to classify between different consonants using CV regions as input. We demonstrate the OAM is correlated with perceptual measures in a variety of contexts including (a) adult dysarthric speech, (b) the speech of children with cleft lip/palate, and (c) a database of accented English speech from native Mandarin and Spanish speakers.
