DIALECT-DRIVEN ASR ERRORS: PHONETIC MISMATCH IN SOUTH ASIAN AMERICAN ENGLISH SPEECH
DOI:
https://doi.org/10.66857/2703Keywords:
Asian American English, ASR bias, phonetic variation, speech recognition errors, dialect mismatch, word error rate, linguistic equity, corpus, computational linguistics.Abstract
ASR systems have reached almost human accuracy with Mainstream American English (MAE), but still make systematic errors on non-mainstream varieties. This paper examines how the ASR errors are formed in South Asian American English (SAAE), and it has been argued that the errors are due to a systematic discrepancy between the phonetic realizations of SAAE speakers and the acoustic-phonetic distributions coded into MAE-trained models, the Phonetic Mismatch Hypothesis. A convergent mixed-methods design was used with controlled speech elicitation and quantitative analysis of error. The 40 SAAE speech samples were put together to form a corpus that reflects major segmental and suprasegmental aspects, such as variation in the quality of vowels, reduction of consonant clusters, epenthesis, and the presence of prosodic transfer. A pretrained Whisper ASR model was tested on reference transcriptions with the calculation of Word Error Rate (WER). A total of 170 errors were identified and classified as substitutions (82; 48.2%), deletions (52; 30.6%), and insertions (36; 21.2%). The speech of SAAE generated a WER of about 43, as opposed to a generation of about 6 by MAE speech, and there was a partial amelioration of the situation when the speech was generated under a fine-tuned adaptation condition (WER ≈ 18%). Types of errors were not randomly distributed among phonetic features: substitution errors were caused by vowel changes and consonant replacements; deletions were explained by the presence of consonant clusters; and most insertions were due to prosodic and rhythmic variation, specifically syllable-timed rhythm and epenthesis. These findings support the phonetic mismatch hypothesis that attributes errors in ASR to linguistic behaviors, and not failures in the system. This study contributes to a phonologically grounded description of ASR bias and proposes training and evaluation models to factor in dialect-specific phonetic knowledge.
