Visual Speech Recognition

Desai, Dhairya; Agrawal, Priyesh; Parikh, Priyansh; Soni, Piyush Kumar; campus, SVKM's Mpstme Nmims Shirpur

doi:10.17577/ijertv9is040509

Cited by 3 publications

(1 citation statement)

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“…In this paper [4], The proposed structure and essential steps are addressed in depth in the four parts that follow. The dynamic lip videos must first be preprocessed, which includes separating audio and video signals, extracting keyframes, and positioning the mouth.…”

Section: Literature Surveymentioning

confidence: 99%

A Brief Survey on Visual Speech Recognition Using CNN

2023

IRJMETS

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The audio-visual speech recognition method using lip movement extracted from side-face images to attempt to increase noise-robustness in mobile environments. Although most previous bimodal speech recognition methods use frontal face (lip) images, these methods are not easy for users since they need to hold a device with a camera in front of their face when talking. Our proposed method capturing lip movement using a small camera installed in a handset is more natural, easy and convenient. Visual features are extracted combined with audio features in the framework of CNN-based recognition.

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Section: Literature Surveymentioning

confidence: 99%

A Brief Survey on Visual Speech Recognition Using CNN

2023

IRJMETS

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Conversion of Audioless Video to Speech Using AV-HuBERT Algorithm

Polavarapu,

Muralidharan,

Harishraj

2024

Information Systems Engineering and Management

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Visual Speech Recognition

Patil¹,

Dhoble²,

Gawade³

et al. 2022

IJARSCT

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The audio-visual speech recognition approach attempts to boost noise-robustness in mobile situations by extracting lip movement from side-face images. Although earlier bimodal speech recognition algorithms used frontal face (lip) images, these approaches are difficult for consumers to utilize because they need them to talk while holding a device with a camera in front of their face. Our proposed solution, which uses a small camera put in a handset to capture lip movement, is more natural, simple, and convenient. This approach also effectively avoids a reduction in the input speech's signal-to-noise ratio (SNR). Optical-flow analysis extracts visual features, which are then coupled with audio features in the context of CNN-based recognition.

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Visual Speech Recognition

Cited by 3 publications

References 0 publications

A Brief Survey on Visual Speech Recognition Using CNN

A Brief Survey on Visual Speech Recognition Using CNN

Conversion of Audioless Video to Speech Using AV-HuBERT Algorithm

Visual Speech Recognition

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