  This paper presents a macroscopic approach to automatic detection of speech
sound disorder (SSD) in child speech. Typically, SSD is manifested by
persistent articulation and phonological errors on specific phonemes in the
language. The disorder can be detected by focally analyzing the phonemes or the
words elicited by the child subject. In the present study, instead of
attempting to detect individual phone- and word-level errors, we propose to
extract a subject-level representation from a long utterance that is
constructed by concatenating multiple test words. The speaker verification
approach, and posterior features generated by deep neural network models, are
applied to derive various types of holistic representations. A linear
classifier is trained to differentiate disordered speech in normal one. On the
task of detecting SSD in Cantonese-speaking children, experimental results show
that the proposed approach achieves improved detection performance over
previous method that requires fusing phone-level detection results. Using
articulatory posterior features to derive i-vectors from multiple-word
utterances achieves an unweighted average recall of 78.2% and a macro F1 score
of 78.0%.
