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Found 8 papers, 1 papers with code
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adapting Pretrained ASR Models to Low-resource Clinical Speech using Epistemic Uncertainty-based Data Selection
While there has been significant progress in ASR, African-accented clinical ASR has been understudied due to a lack of training datasets.
AfriNames: Most ASR models "butcher" African Names
Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a laboratory result for a patient.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On the diminishing return of labeling clinical reports
Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains.
Learning to estimate label uncertainty for automatic radiology report parsing
Bootstrapping labels from radiology reports has become the scalable alternative to provide inexpensive ground truth for medical imaging.
Caveats in Generating Medical Imaging Labels from Radiology Reports
Acquiring high-quality annotations in medical imaging is usually a costly process.
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction
Obtaining datasets labeled to facilitate model development is a challenge for most machine learning tasks.
