Towards automated detection of similarities and differences in bilingual speakers

Abstract

Bilingualism is increasingly the norm in the United States with at least 20% of Americans being bilingual. This change in linguistic demography has created a new challenge to accurately diagnose speech disorders among bilingual children. This study explores Acoustic Landmark Detection (ALD) system as an objective approach to characterizing similarities and differences in speech production in child speakers of Standard English and Jamaican Creole (JC). Eight JC-English bilingual children were recorded speaking eleven words three times in each language. Words were transcribed and entered into PROPH + to provide: (1) Phonological Mean Length of Utterance [pMLU], (2) phonotactic structure, and (3) Percent Consonants Correct (PCC). Landmarks were hand-marked to determine probable landmark sequences based on canonical word production. Canonical landmark sequences were aligned with detected landmark sequences in both languages using the Needleman-Wunsch global alignment algorithm. Analysis revealed that if PCC indicates JC and English words are different, the mismatch between landmark tends to be lower (higher production accuracy); if phonotactics indicate JC and English words are different, mismatch tends to be slightly higher (lower accuracy); and if pMLU indicates word differences, mismatch tends to be higher (lower accuracy). Our finding support traditional linguistic expectations regarding bilingual speakers’ similarities and differences.