  Speech sound disorder (SSD) refers to a type of developmental disorder in
young children who encounter persistent difficulties in producing certain
speech sounds at the expected age. Consonant errors are the major indicator of
SSD in clinical assessment. Previous studies on automatic assessment of SSD
revealed that detection of speech errors concerning short and transitory
consonants is less satisfactory. This paper investigates a neural network based
approach to detecting consonant errors in disordered speech using
consonant-vowel (CV) diphone segment in comparison to using consonant monophone
segment. The underlying assumption is that the vowel part of a CV segment
carries important information of co-articulation from the consonant. Speech
embeddings are extracted from CV segments by a recurrent neural network model.
The similarity scores between the embeddings of the test segment and the
reference segments are computed to determine if the test segment is the
expected consonant or not. Experimental results show that using CV segments
achieves improved performance on detecting speech errors concerning those
"difficult" consonants reported in the previous studies.
