06-16-2022, 10:17 AM
Current deep learning systems are hence ineffective in such environments. To address the issue, we use the estimated 3D speaker geometry and video audio transcriptions to generate a large, acoustic model based audio dataset that can be used to train a bilingual speech recognition system for this collaborative learning environment. As we demonstrate in this paper, although we train on synthetic datasets, we are still able to match and slightly exceed state-of-the-art systems. The current paper significantly extends our previous research on analyzing such videos. 3D speaker geometry from the AOLME videos. The paper uses video object detection and projective geometry to locate the 3D speaker geometry from still video frames. We use the audio transcriptions with the AWS text-to-speech system to generate the ground truth audio datasets for training our speech recognition system. 26.12%. In addition, the Bilingual Keyword Classifier obtained an average of 38% sensitivity on Spanish Keywords. The rest of the paper is organized as follows. We use projective geometry to estimate 3D coordinates from still image frames.
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