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Boyla Mainsah

4 accepted papers

2025

The Omni-Expert: A Computationally Efficient Approach to Achieve a Mixture of Experts in a Single Expert Model

NeurIPS 2025poster

Mixture-of-Experts (MoE) models have become popular in machine learning, boosting performance by partitioning tasks across multiple experts. However, the need for several experts often results in high computational costs, limiting their application on resource-constrained devices with stringent real…

Cited by 0SourceScholar
2021

A Causal Deep Learning Framework for Classifying Phonemes in Cochlear Implants

ICASSP 2021accepted

Speech intelligibility in cochlear implant (CI) users degrades considerably in listening environments with reverberation and noise. Previous research in automatic speech recognition (ASR) has shown that phoneme-based speech enhancement algorithms improve ASR system performance in reverberant environ…

Cited by 0SourceScholar
2020

Using Automatic Speech Recognition and Speech Synthesis to Improve the Intelligibility of Cochlear Implant users in Reverberant Listening Environments

ICASSP 2020accepted

Cochlear implant (CI) users experience substantial difficulties in understanding reverberant speech. A previous study proposed a strategy that leverages automatic speech recognition (ASR) to recognize reverberant speech and speech synthesis to translate the recognized text into anechoic speech. Howe…

Cited by 0SourceScholar
2018

Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces

NeurIPS 2018poster

Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using a sequence of sensory stimuli to elicit specific neural responses as control signals, while a user attends to relevant target stimuli that occur within the sequence. In current BCIs, the stimulus presentation s…

Cited by 7SourcePDFScholar