Training in Temporal Information Processing Ameliorates Phonetic Identification

Abstract

Many studies revealed a link between temporal information processing (TIP) in a millisecond range and speech perception. Previous studies indicated a dysfunction in TIP accompanied by deficient phonemic hearing in children with specific language impairment (SLI). In this study we concentrate in SLI on phonetic identification, using the voice-onset-time (VOT) phenomenon in which TIP is built-in. VOT is crucial for speech perception, as stop consonants (like /t/ vs. /d/) may be distinguished by an acoustic difference in time between the onsets of the consonant (stop release burst) and the following vibration of vocal folds (voicing). In healthy subjects two categories (voiced and unvoiced) are determined using VOT task. The present study aimed at verifying whether children with SLI indicate a similar pattern of phonetic identification as their healthy peers and whether the intervention based on TIP results in improved performance on the VOT task. Children aged from 5 to 8 years (n = 47) were assigned into two groups: normal children without any language disability (NC, n = 20), and children with SLI (n = 27). In the latter group participants were randomly classified into two treatment subgroups, i.e., experimental temporal training (EG, n = 14) and control non-temporal training (CG, n = 13). The analyzed indicators of phonetic identification were: (1) the boundary location (α) determined as the VOT value corresponding to 50% voicing/unvoicing distinctions; (2) ranges of voiced/unvoiced categories; (3) the slope of identification curve (β) reflecting the identification correctness; (4) percent of voiced distinctions within the applied VOT spectrum. The results indicated similar α values and similar ranges of voiced/unvoiced categories between SLI and NC. However, β in SLI was significantly higher than that in NC. After the intervention, the significant improvement of β was observed only in EG. They achieved the level of performance comparable to that observed in NC. The training-related improvement in CG was non-significant. Furthermore, only in EG the β values in post-test correlated with measures of TIP as well as with phonemic hearing obtained in our previous studies. These findings provide another evidence that TIP is omnipresent in language communication and reflected not only in phonemic hearing but also in phonetic identification.

Keywords: phonetic identification; specific language impairment (SLI); temporal information processing (TIP); temporal intervention; voice-onset-time; voicing contrast detection.

FIGURE 1

Waveforms of two words from the endpoints of applied VOT spectrum: (A) VOT of –90 ms identified as DOMEK and (B) Voice-onset-time (VOT) of 20 ms identified as TOMEK.

FIGURE 2

The response card used in VOT test.

FIGURE 3

Schema of the study. In NC the VOT task was performed once, whereas in EG and CG subgroups all three assessment tasks: VOT, Phoneme Discrimination Test (PDT), and temporal information processing (TIP) were conducted twice, i.e., before and after the intervention.

FIGURE 4

Schema of performed comparisions in Steps 1–3, where (1) reflects camparisons of phonetic identification between NC and SLI, (2) training-related differences in EG and CG, and (3) post-test performance in EG and CG in comparison to NC.

FIGURE 5

The sigmoid function for NC and specific language impairment (SLI) groups. The boundary location (α) corresponds to the VOT value at which 50% of voiced/unvoiced identifications were detected.

FIGURE 6

The voiced identification score for the presented VOT spectrum in NC and SLI indicating the similar ranges of voiced/unvoiced categories in both groups with poorer performance in SLI than in NC.

FIGURE 7

The sigmoid function for EG and CG subgroups in pre- and post-test performance. The boundary location (α) corresponds to the VOT value at which 50% of voiced/unvoiced identifications were detected.

FIGURE 8

The sigmoid function for post-test performance of EG and CG subgroups in comparison to NC. The boundary location (α) corresponds to the VOT value at which 50% of voiced/unvoiced identifications were detected.