Search Results for author:
Found 12 papers, 3 papers with code
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
To overcome this, we propose DP-2Stage, a two-stage fine-tuning framework for differentially private tabular data generation.
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data.
Performant ASR Models for Medical Entities in Accented Speech
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis
Recent advances in speech synthesis have enabled many useful applications like audio directions in Google Maps, screen readers, and automated content generation on platforms like TikTok.
The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation
We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection, and the resultant regionally diverse World Wide Dishes evaluation dataset.
Towards Biologically Plausible and Private Gene Expression Data Generation
In this paper, we initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data.
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)
+2
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
The potential of realistic and useful synthetic data is significant.
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
Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges
These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) on December 14th, 2021.
Learning Nigerian accent embeddings from speech: preliminary results based on SautiDB-Naija corpus
We describe how the corpus was created and curated as well as preliminary experiments with accent classification and learning Nigerian accent embeddings.
Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience
These are the proceedings of the 4th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) on Saturday, December 12th 2020.
