342 citations · 700 across the 14 of their papers we have counts for
16 papers
The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100
Francesco Mantegna, Gereon Elvers, Dulhan Jayalath +18
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to progress from foundational tasks…
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau, Miran Özdogan, Gereon Elvers +15
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising applications is the restoration of communicat…
Imagen 3
Imagen-Team-Google, :, Jason Baldridge +257
We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred…
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
Dulhan Jayalath, Gilad Landau, Brendan Shillingford +2
The past few years have seen remarkable progress in the decoding of speech from brain activity, primarily driven by large single-subject datasets. However, due to individual variat…
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text
Oiwi Parker Jones, Brendan Shillingford
In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for ab…
More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech
Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford +3
In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes advantage of video frames as an additional input alongside text, and gener…
Interactive decoding of words from visual speech recognition models
Brendan Shillingford, Yannis Assael, Misha Denil
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…
Large-scale multilingual audio visual dubbing
Yi Yang, Brendan Shillingford, Yannis Assael +9
We describe a system for large-scale audiovisual translation and dubbing, which translates videos from one language to another. The source language's speech content is transcribed…
Recurrent Neural Network Transducer for Audio-Visual Speech Recognition
Takaki Makino, Hank Liao, Yannis Assael +4
This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture. To support the development of such a sy…
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini +2
To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for the…
Speech bandwidth extension with WaveNet
Archit Gupta, Brendan Shillingford, Yannis Assael +1
Large-scale mobile communication systems tend to contain legacy transmission channels with narrowband bottlenecks, resulting in characteristic "telephone-quality" audio. While high…
Sample Efficient Adaptive Text-to-Speech
Yutian Chen, Yannis Assael, Brendan Shillingford +11
We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and ind…
Large-Scale Visual Speech Recognition
Brendan Shillingford, Yannis Assael, Matthew W. Hoffman +12
This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consist…
Cortical microcircuits as gated-recurrent neural networks
Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford +2
Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common co…
LipNet: End-to-End Sentence-level Lipreading
Yannis M. Assael, Brendan Shillingford, Shimon Whiteson +1
Lipreading is the task of decoding text from the movement of a speaker's mouth. Traditional approaches separated the problem into two stages: designing or learning visual features,…
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez +5
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In th…