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Edged based Audio-Visual Speech enhancement demonstrator

  • Song Chen
  • , Mandar Gogate
  • , Kia Dashtipour
  • , Jasper Kirton-Wingate
  • , Adeel Hussain
  • , Faiyaz Doctor
  • , Tughrul Arslan
  • , Amir Hussain

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Difficulty understanding speech in noisy environments presents a significant challenge for individuals with hearing loss and is a primary factor contributing to non-adherence to hearing aid use. Recent technological advancements integrating artificial intelligence, machine learning, and smartphone technology hold promise in advancing and customizing hearing healthcare. A proposed solution is a portable hearing assistive system designed for speech enhancement in noisy settings. We anticipate that this system will enhance the auditory experience of hearing aid users. The system leverages a mobile phone's camera, microphone, and speaker, ensuring ease of portability. Raw video and audio data are stored locally on the phone and processed by the device's processor alongside an audio-visual speech enhancement algorithm. This algorithm is capable of identifying voice signals and lip movements using a lightweight deep neural network model, thereby optimizing memory efficiency required for real-time processing.

Original languageEnglish
Pages (from-to)2032-2033
Number of pages2
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2024
Externally publishedYes
Event25th Interspeech Conferece 2024 - Kos Island, Greece
Duration: 1 Sep 20245 Sep 2024

Bibliographical note

Publisher Copyright:
© 2024 International Speech Communication Association. All rights reserved.

Keywords

  • AudioVisual
  • Deep Learning
  • Speech Enhancement

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Language and Linguistics
  • Modeling and Simulation
  • Human-Computer Interaction

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