Search Results for author:
Found 17 papers, 7 papers with code
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact.
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
The past few years have seen remarkable progress in the decoding of speech from brain activity, primarily driven by large single-subject datasets.
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text
In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.
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
More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech
In this paper we present VDTTS, a Visually-Driven Text-to-Speech model.
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.
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.
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 #10 on
Audio-Visual Speech Recognition
on LRS3-TED
(using extra training data)
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions.
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.
Recovering Missing Characters in Old Hawaiian Writing
In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.
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.
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 #19 on
Lipreading
on LRS3-TED
(using extra training data)
Cortical microcircuits as gated-recurrent neural networks
Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas.
LipNet: End-to-End Sentence-level Lipreading
Lipreading is the task of decoding text from the movement of a speaker's mouth.
Ranked #5 on
Lipreading
on GRID corpus (mixed-speech)
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine learning has been wildly successful.
