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Recent strides in automatic speech recognition (ASR) have accelerated their application in the medical domain where their performance on accented medical named-entities (NE) such as drug names, diagnoses, and lab results, is largely unknown. We rigorously evaluate multiple ASR models on a clinical English dataset of 120 African accents. Our analysis reveals that despite some models achieving low overall Word Error Rates (WER), errors in clinical entities are higher, potentially posing substantial risks to patient safety. To empirically demonstrate this, we extract clinical entities from transcripts, align ASR predictions with these entities, and compute Medical NE Recall, Medical WER, and Character Error Rate, novel metrics specifically developed to assess medical NE Recognition performance in speech. We demonstrate that finetuning on accented clinical speech improves Medical-WER by a wide margin (36% absolute), improving their practical applicability in healthcare environments

Despite advancements in speech recognition, accented speech remains challenging. While previous approaches have focused on modeling techniques or creating accented speech datasets, gathering sufficient data for the multitude of accents, particularly in the African context, remains impractical due to their sheer diversity and associated budget constraints. To address these challenges, we propose AccentFold, a method that exploits spatial relationships between learned accent embeddings to improve downstream Automatic Speech Recognition (ASR). Our exploratory analysis of speech embeddings representing 100+ African accents reveals interesting spatial accent relationships highlighting geographic and genealogical similarities, capturing consistent phonological, and morphological regularities, all learned empirically from speech. Furthermore, we discover accent relationships previously uncharacterized by the Ethnologue. Through empirical evaluation, we demonstrate the effectiveness of AccentFold by showing that, for out-ofdistribution (OOD) accents, sampling accent subsets for training based on AccentFold information outperforms strong baselines with a relative WER improvement of 4.6%. AccentFold presents a promising approach for improving ASR performance on accented speech, particularly in the context of African accents, where data scarcity and budget constraints pose significant challenges. Our findings emphasize the potential of leveraging linguistic relationships to improve zero-shot ASR adaptation to target accents. Please find our code for this work here.1