Facebook parent company Meta Platforms Inc. is trying to tackle one of the biggest problems in artificial intelligence-based speech recognition: background noise.
Modern AI speech recognition systems don’t always work that well in situations where there’s lots of noise, or if multiple people are speaking at the same time. They generally use sophisticated noise-suppression techniques to try to filter out that noise, but those are often no match for the human ability to combine hearing with vision.
To solve the problem, Meta AI has created a new conversational AI framework, called Audio-Visual Hidden Unit BERT, which aims to train AI models by both hearing and seeing people speak.
Meta AI said AV-HuBERT is the first system of its kind that can jointly model speech and lip movements from unlabelled video that hasn’t been transcribed.
Read More: https://siliconangle.com/2022/01/07/meta-ai-built-speech-recognition-platform-relies-visual-cues-filter-background-noise/
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