Login / Signup

Transcribing multilingual children's and adults' speech.

Kate MargetsonSharynne Lindy McLeodSarah VerdonVan H Tran
Published in: Clinical linguistics & phonetics (2022)
Speech-language pathologists (SLPs) face challenges in transcription and diagnosis of speech sound disorders (SSD) in multilingual children due to ambient language influences and cross-linguistic transfer. The VietSpeech Multilingual Transcription Protocol, a 4-step process to undertake impressionistic transcription of multilingual speech was tested using data from Vietnamese-Australian children ( n = 69) and adult family members ( n = 85). The transcription team included an English-speaking SLP, a Vietnamese-English-speaking linguist and accredited interpreter, and two Vietnamese-English-speaking SLPs. (1) Training: The team completed training together in Vietnamese and English phonology. (2) Speech assessment: The participants were assessed using the Diagnostic Evaluation of Articulation and Phonology (DEAP) in English and the Vietnamese Speech Assessment (VSA). (3) Transcription comparison: Inter-rater reliability for 10 children and 12 adults was calculated using consonant-by-consonant agreement. For English the 3-way inter-rater agreement was 92.62% for children and 88.69% for adults. For Vietnamese the 4-way inter-rater agreement was 86.57% for children and 96.05% for adults. There was a significant correlation between speech accuracy and inter-rater reliability for children's consonants in English ( r = 0.95) and Vietnamese ( r = 0.91), and adults' consonants in English ( r = 0.90), but not for Vietnamese ( r = 0.49). Reliability was influenced by phoneme class and whether the target consonant was shared between languages. (4) Rule generation and consensus: Rules based on near functional equivalence were implemented to maintain consistency and reach consensus. SLPs who do not speak clients' home languages can be supported to transcribe multilingual speech by working with multilingual teams, and understanding personal limitations during multilingual speech assessments.
Keyphrases
  • young adults
  • transcription factor
  • randomized controlled trial
  • hearing loss
  • air pollution
  • machine learning
  • deep learning
  • human immunodeficiency virus
  • virtual reality
  • clinical evaluation
  • antiretroviral therapy