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The recent emergence of large pretrained ASR models has facilitated multiple transfer learning and domain adaptation efforts, in which performant general-purpose ASR models are fine-tuned for specific domains, such as clinical or accented speech. However, African accented clinical speech recognition remains largely unexplored. We propose a semantically aligned, domain-specific multitask learning framework (generative and discriminative) and demonstrate empirically that semantically aligned, multitask learning enhances ASR, outperforming the single-task architecture by 2.5% (relative). We discover that the generative multitask design improves generalization to unseen accents, while the discriminative multitask approach improves clinical ASR for majority and minority accents.

but productivity tools such as clinical automatic speech recognition (ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, in developed nations, and clinician-reported performance of commercial clinical ASR systems is generally satisfactory. Furthermore, the recent performance of general domain ASR is approaching human accuracy. However, several gaps exist. Several publications have highlighted racial bias with speech-to-text algorithms and performance on minority accents lags significantly. To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents. We release AfriSpeech, 200hrs of Pan-African speeches, 67,577 clips from 2,463 unique speakers, across 120 indigenous accents from 13 countries for clinical and general domain ASR, a benchmark test set, with publicly available pre-trained models with SOTA performance on the AfriSpeech benchmark.

Building robust ASR systems in this domain requires large amounts of annotated data for a wide variety of linguistically and morphologically rich accents, which are expensive to create. Our study aims to address this problem by reducing the annotation cost through informative data selection using active learning. We show that incorporating epistemic uncertainty into our active learning training loops achieves state-of-the-art results while reducing the amount of labeled data and annotation costs by 40% ($130,000+). Our approach also improves out-of-distribution generalization for very low-resource accents, demonstrating the viability of active learning for building generalizable ASR models in the context of accented African clinical ASR, where training datasets are predominantly scarce.