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Speech delay is a childhood language problem that sometimes is resolved on its own but sometimes may cause more serious language difficulties later. This leads therapists to screen children for detection at early ages in order to eliminate future problems. Using the Goldman-Fristoe Test of Articulation (GFTA) method, therapists listen to a child's pronunciation of certain phonemes and phoneme pairs in specified words and judge the child's stage of speech development. The goal of this paper is to develop an Automatic Speech Recognition (ASR) tool and related speech processing methods which emulate the knowledge of speech therapists. In this paper two methods of feature extraction (MFCC and DCTC) were used as the baseline for training an HMM-based utterance verification system which was later used for testing the utterances of 63 young children (ages 4-10), both typically developed and speech delayed. The ASR results show the value of augmenting static spectral information with spectral trajectory information for better prediction of therapist's judgments.