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20212024
most citedMultimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement

45 citations · 69 across the 12 of their papers we have counts for

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6 papers · 1 filter

eess.AS2024★ 1 cited

Audio-Visual Speech Enhancement in Noisy Environments via Emotion-Based Contextual Cues

Tassadaq Hussain, Kia Dashtipour, Yu Tsao +1

In real-world environments, background noise significantly degrades the intelligibility and clarity of human speech. Audio-visual speech enhancement (AVSE) attempts to restore spee…

eess.AS2023

Deep Complex U-Net with Conformer for Audio-Visual Speech Enhancement

Shafique Ahmed, Chia-Wei Chen, Wenze Ren +7

Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems. In this paper, we introduce a novel audio-visual SE…

eess.AS2023★ 1 cited

Leveraging Self-Supervised Audio-Visual Pretrained Models to Improve Vocoded Speech Intelligibility in Cochlear Implant Simulation

Richard Lee Lai, Jen-Cheng Hou, I-Chun Chern +6

Individuals with hearing impairments face challenges in their ability to comprehend speech, particularly in noisy environments. The aim of this study is to explore the effectivenes…

eess.AS2022

A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids

Abhijeet Bishnu, Ankit Gupta, Mandar Gogate +5

In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low laten…

eess.AS2022★ 1 cited

Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings

I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen +5

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests…

eess.AS2022★ 1 cited

A Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning for Hearing-Assistive Technologies

Tassadaq Hussain, Muhammad Diyan, Mandar Gogate +4

Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are generally trained to minimise the distance between clean and enhanced sp…