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Found 9 papers, 3 papers with code
Restoring and attributing ancient texts using deep neural networks
Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history.
Ranked #1 on
Ancient Text Restoration
on I.PHI
Restoring ancient text using deep learning: a case study on Greek epigraphy
Ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts, for evidence of the recorded past.
Recurrent Neural Network Transducer for Audio-Visual Speech Recognition
This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture.
Ranked #5 on
Audio-Visual Speech Recognition
on LRS3-TED
(using extra training data)
Large-Scale Visual Speech Recognition
To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3, 886 hours of video).
Ranked #11 on
Lipreading
on LRS3-TED
(using extra training data)
Sample Efficient Adaptive Text-to-Speech
Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
Speech bandwidth extension with WaveNet
This paper proposes an approach where a communication node can instead extend the bandwidth of a band-limited incoming speech signal that may have been passed through a low-rate codec.
Large-scale multilingual audio visual dubbing
The visual content is translated by synthesizing lip movements for the speaker to match the translated audio, creating a seamless audiovisual experience in the target language.
Interactive decoding of words from visual speech recognition models
This work describes an interactive decoding method to improve the performance of visual speech recognition systems using user input to compensate for the inherent ambiguity of the task.
Evaluating Frontier Models for Dangerous Capabilities
To understand the risks posed by a new AI system, we must understand what it can and cannot do.
