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Welcome to Bio-RAMP Lab!

Biomedical Research in Artificial Intelligence and Machine Perception (Bio-RAMP) is a global multidisciplinary research community at the intersection of healthcare and artificial intelligence. Our goal is to provide a platform for researchers across the globe to contribute to the development and application of AI in the field of medicine. Our lab is committed to increasing the participation and engagement of researchers and communities that are currently underrepresented in this field. We believe that the diversity of perspectives and experiences will lead to more inclusive and impactful solutions.

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Papers

Under Review

AccentFold: A Linguistic Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents

  • Empirical Methods in Natural Language Processing '23

Abstract

Despite advancements in speech recognition001 technology, accented speech still poses difficulties. Previous approaches have focused on modeling techniques or creating accented speech datasets, but gathering sufficient data for the multitude of accents, particularly in the context of African accents, remains impractical due to their sheer diversity and associated budget constraints.

Our work, in contrast, explores the under-explored area of African accent linguistics. We propose AccentFold, a method that utilizes learned accent embeddings to explore linguistic regularities between accents.  Our exploratory analysis of AccentFold provides insights into the spatial relationships between accents and reveals that accent embeddings group together based on geographic and language family similarities, capturing phonological, and morphological regularities based on language families. Furthermore, we reveal two interesting relationships in some African accents that have been uncharacterized by the Ethnologue. Through empirical evaluation, we demonstrate the effectiveness of AccentFold by showing that selecting accent subsets for training based on AccentFold information outperforms random selection for zero shot ASR on target accents. With an average WER improvement of 3.5%, 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.

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Under Review

Building Cost-Efficient, Robust and Linguistically Diverse Automatic Speech Recognition Systems in Pan-African Clinical Context

  • 61st Annual Meeting of the Association for Computational Linguistics (ACL'23)

Abstract

While there has been significant progress in ASR, African-accented clinical ASR has been understudied due to a lack of training datasets.

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.

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In Progress

Approved for Who: a scoping review on Fairness in 500+ FDA-Approved AI Algorithms

  • Algorithm Bias/Fairness

Abstract

A scoping review on fairness in 500+ FDA-Approved AI Algorithms
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Researchers

Bonaventure F. P. Dossou

Computer Science
Ph.D.
  • McGill University
  • Canada
Linkedin-in Github

Moshood Yekini

Machine Intelligence
M.Sc
  • African Masters in Machine Intelligence, AMMI
  • Nigeria
Linkedin-in Github

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Are you interested in becoming a part of a community of researchers working at the intersection of healthcare and machine learning? Are you eager to publish or build projects in this area? Join us!

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