Vowel errors produced by preschool-age children on a single-word test of articulation

Vowel distinctiveness as a concurrent predictor of expressive language function in autistic children

Speech ability may limit spoken language development in some minimally verbal autistic children. In this study, we aimed to determine whether an acoustic measure of speech production, vowel distinctiveness, is concurrently related to expressive language (EL) for autistic children. Syllables containing the vowels [i] and [a] were recorded remotely from 27 autistic children (4;1-7;11) with a range of spoken language abilities. Vowel distinctiveness was calculated using automatic formant tracking software. Robust hierarchical regressions were conducted with receptive language (RL) and vowel distinctiveness as predictors of EL. Hierarchical regressions were also conducted within a High EL and a Low EL subgroup. Vowel distinctiveness accounted for 29% of the variance in EL for the entire group, RL for 38%. For the Low EL group, only vowel distinctiveness was significant, accounting for 38% of variance in EL. Conversely, in the High EL group, only RL was significant and accounted for 26% of variance in EL. Replicating previous results, speech production and RL significantly predicted concurrent EL in autistic children, with speech production being the sole significant predictor for the Low EL group and RL the sole significant predictor for the High EL group. Further work is needed to determine whether vowel distinctiveness longitudinally, as well as concurrently, predicts EL. Findings have important implications for the early identification of language impairment and in developing language interventions for autistic children.

Speech Error Variability and Phonological Awareness in Preschoolers

PURPOSE

The purpose of this article was to investigate the relationship between speech error variability and phonological awareness.

METHOD

This article begins with a narrative review of the theoretical interpretation of speech error variability. The post hoc exploratory analysis of the relationship between speech error variability and phonological awareness included 40 children: 20 with typical speech and language and 20 with speech sound disorder and typical language. Groups were matched on gender, age, maternal education, receptive and expressive vocabulary, nonverbal intelligence, and expressive morphosyntax. Multiple regression was used to identify the best fit model for the relationship between vocabulary, speech errors, and phonological awareness.

RESULTS

Segmental variability was associated with poor phonological awareness in preschool-aged children.

CONCLUSION

Children with high levels of segmental variability have poor phonological awareness, likely due to unstable phonological representations.

Process-Oriented Profiling of Speech Sound Disorders

The differentiation between subtypes of speech sound disorder (SSD) and the involvement of possible underlying deficits is part of ongoing research and debate. The present study adopted a data-driven approach and aimed to identify and describe deficits and subgroups within a sample of 150 four to seven-year-old Dutch children with SSD. Data collection comprised a broad test battery including the Computer Articulation Instrument (CAI). Its tasks Picture Naming (PN), NonWord Imitation (NWI), Word and NonWord Repetition (WR; NWR) and Maximum Repetition Rate (MRR) each render a variety of parameters (e.g., percentage of consonants correct) that together provide a profile of strengths and weaknesses of different processes involved in speech production. Principal Component Analysis on the CAI parameters revealed three speech domains: (1) all PN parameters plus three parameters of NWI; (2) the remaining parameters of NWI plus WR and NWR; (3) MRR. A subsequent cluster analysis revealed three subgroups, which differed significantly on intelligibility, receptive vocabulary, and auditory discrimination but not on age, gender and SLPs diagnosis. The clusters could be typified as three specific profiles: (1) phonological deficit; (2) phonological deficit with motoric deficit; (3) severe phonological and motoric deficit. These results indicate that there are different profiles of SSD, which cover a spectrum of degrees of involvement of different underlying problems.

An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

For Thai vowel pronunciation, it is very important to know that when mispronunciation occurs, the meanings of words change completely. Thus, effective and standardized practice is essential to pronouncing words correctly as a native speaker. Since the COVID-19 pandemic, online learning has become increasingly popular. For example, an online pronunciation application system was introduced that has virtual teachers and an intelligent process of evaluating students that is similar to standardized training by a teacher in a real classroom. This research presents an online automatic computer-assisted pronunciation training (CAPT) using deep learning to recognize Thai vowels in speech. The automatic CAPT is developed to solve the inadequacy of instruction specialists and the complex vowel teaching process. It is a unique system that develops computer techniques integrated with linguistic theory. The deep learning model is the most significant part of recognizing vowels pronounced for the automatic CAPT. The major challenge in Thai vowel recognition is the correct identification of Thai vowels when spoken in real-world situations. A convolutional neural network (CNN), a deep learning model, is applied and developed in the classification of pronounced Thai vowels. A new dataset for Thai vowels was designed, collected, and examined by linguists. The result of an optimal CNN model with Mel spectrogram (MS) achieves the highest accuracy of 98.61%, compared with Mel frequency cepstral coefficients (MFCC) with the baseline long short-term memory (LSTM) model and MS with the baseline LSTM model have an accuracy of 94.44% and 90.00% respectively.