Clinical Applicability of Whisper-Based Automatic Transcription for Korean Speech Audiometry Sentence Tests in Older Adults with Hearing Loss

Presbycusis often disrupts sentence-level communication, while clinical speech audiometry still depends on labor-intensive scoring.This exploratory two-case study examined whether the large-scale automatic speech recognition (ASR) model Whisper large-v3 can reliably transcribe sentence recognition performances in elderly listeners with presbycusis and how agerelated auditory and speech characteristics shape its error patterns.Two native Korean elderly listeners with symmetric sensorineural hearing loss (S-001: moderate steeply sloping; S-002: mild sloping highfrequency loss) completed the Korean Speech Audiometry (KSA) sentence test (80 sentences).Their repetitions in the quiet were recorded and independently transcribed by four experienced audiologists.Expert transcriptions were compared with Whisper outputs generated under fixed decoding parameters and standardized text normalization, using sentence match rate, word error rate (WER), and character error rate (CER).Whisper showed relatively low CER (about 8%-15%) but substantially higher word-and sentence-level errors (WER 30% for S-001 vs. 18% for S-002; sentence match 38.5% vs. 71.2%).Errors clustered in the high-frequency fricatives/affricates, final consonants, low-frequency and polysyllabic words, and longer syntactically complex sentences.Better clinical speech audiometry scores (KSA sentence/word recognition and word recognition score) were associated with higher ASR sentence match rates and lower WER/CER across the two cases.Generic ASR partially agreed with expert transcriptions, suggesting potential as a complementary tool, but elderlyand hearing loss-tailored ASR models and test designs are needed for reliable AI-based sentence recognition.