Objectives: This study aims to examine Korean and English research trends regarding language development and language impairment, and to suggest future directions for later research. Methods: Sixty-five Korean research papers published in Communication Science & Disorders and 129 English research published in the Journal of Speech, Language, and Hearing Research were classified according to the subject, time dimension, observational method, independent variable, and dependent variable. The frequency and rate of publication of each type were yielded. Results: Nearly half of the subjects were typically developing toddlers, but there were some differences between the research in the two journals. Cross-sectional studies had higher rates in Korean research, and longitudinal studies had higher rates in English research. In Korean journals, most articles were descriptive studies, with very few experimental studies. In English journals, various independent variables were used relatively evenly, whereas the variables related to the characteristics of the subject were used at higher rates in the Korean journals. Both journals mostly used variables associated with expressive language as dependent variables. Conclusion: Although more research has been published and the types of studies have become more diverse than in the past, during the recent decade in Korea the amount and types of studies are limited when compared to English research. In this regard, this study discussed the direction in which future research related to infant language disorder should proceed.

Objectives: Language sample analysis (LSA) is a critical component of child language assessment. However, most clinicians consider LSA to be time consuming work. In particular, transcription is seen as an overwhelming task. Due to rapid technological advances, various automatic speech recognition systems have been developed. This study aimed to investigate the accuracy and the characteristics of two automatic speech recognition programs, Naver Clova Speech (Naver Clova) and Google Speech-to-Text (STT).Methods: A total of 40 school-aged children with typical development (TD) and children with language learning disabilities (LLD) participated in the study. Each child was asked to generate two fictional narratives. In total, 72 narratives produced by 36 children were used. To examine the accuracy of Naver Clova and Google STT, syllable error rate was analyzed and compared to reference transcripts. For the detailed analysis, types of error such as substitution, deletion and insertion were examined.Results: Results showed that Naver Clova was significantly lower than Google STT in error rate of transcription. But the transcription error rate of the two child groups was not significantly different. Additionally, the Naver Clova error rate was higher in substitution, deletion, and insertion respectively. The Google STT error rate, on the other hand, was higher in deletion, substitution and insertion respectively.Conclusion: Naver Clova were more accurate than Google STT in transcribing children’s narratives. But the transcription accuracy of two child groups was not different. This suggests that recently developed automatic speech recognition systems have clinical utility. These systems can reduce clinician’s workload in regards to LSA and this would contribute to qualitatively enhanced language assessment.