A phonemic implicational feature hierarchy of phonological contrasts for English-speaking children.

Contrastive feature hierarchies have been developed and used for some time in depicting typical phonological development and in guiding therapy decisions. Previous descriptions of feature use have been based on independent analyses and usually phonetic inventories. However, recent trends in phonology include a relational analysis of phonemic inventories (D. Ingram & K. D. Ingram, 2001). The current investigation was a relational analysis of the phonemic inventories of 40 typically developing 2-year-old American-English-speaking children. Consonant inventories were derived from spontaneous speech samples using the Logical International Phonetics Programs computer software (D. K. Oller & R. E. Delgado, 1999). Cluster analysis was used to determine the grouping of contrastive features. Four levels emerged. Level I included [consonant], [sonorant], and [coronal], Level II included [voice], Level III included [anterior], [continuant], and [nasal], and Level IV included [lateral] and [strident]. Results suggested that the resulting 4-level phonemic feature hierarchy might be used to classify the phonological systems of children with phonological disorders.

Phonological profiles of 2-year-olds with delayed language development: predicting clinical outcomes at age 3.

Twenty-eight 2-year-olds were screened for language development using the Language Development Survey (LDS; L. Rescorla, 1989) and underwent a clinical evaluation within a month following LDS administration. Six measures of phonological development were derived from 20-min language samples of parent-child play interactions, including number of different consonants, number of different consonants in the initial and final positions, number of different consonant clusters in the initial and final position, and percentage of closed syllables shapes. Comparisons were made among 3 groups: (a) those who screened positive on the LDS (LDS+) who were within normal limits on follow-up, (b) those who were identified as language delayed (LD), and (c) children who were language normal (LN). Results revealed that children who were LDS+ and LD had comparable phonetic profiles. The LD group had significantly lower scores on all phonetic measures tested, as compared to the LN group. Clinical recommendations were available on a subset of 13 children who were reassessed at age 3. Children who received "monitor" or "treatment" recommendations at age 3 had significantly lower z scores on measures of phonetic development recorded at 2 years of age than toddlers who received a recommendation of "no concerns" at 3 years. Our preliminary findings indicated that the more delayed the 2-year-old child was in phonological development, the more at risk the child was for continuing delays at age 3.

The effect of noise on computer-aided measures of voice: a comparison of CSpeechSP and the Multi-Dimensional Voice Program software using the CSL 4300B Module and Multi-Speech for Windows.

The effect of noise on computer-derived samples of voice was compared across three different hardware/software configurations. The hardware/software systems included a stand-alone A/D converter (CSL Module 4300B) coupled to a custom Pentium PC used in conjunction with the Multi-Dimensional Voice Program (MDVP) software, and a Creative Labs A/D converter coupled to the same custom PC under software control of MDVP/Multispeech and CSpeechSP. Voice samples were taken from 10 female subjects, then mixed with computer fan noise creating three different signal-to-noise (S/N) levels. Mixed signals were analyzed on the three hardware/software systems. Results revealed that fundamental frequency was most resistant to the degradation effect of noise across systems; jitter and shimmer values, however, were more variable across all configurations. Jitter and shimmer values were significantly higher under certain S/N levels for the MDVP 4300B based system as compared to MDVP for Multi-Speech and CSpeechSP. The findings punctuate the need for sensitivity to recording environments, careful selection of hardware/software equipment arrays, and the establishment of minimal recording conditions (>25 dBA S/N) for voice sampling and analysis using computer-assisted methods.