Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

Audio deepfake detection is an emerging active topic. A growing number of literatures have
aimed to study deepfake detection algorithms and achieved effective performance, the …

We show for the first time that learning powerful representations from speech audio alone
followed by fine-tuning on transcribed speech can outperform the best semi-supervised …

We introduce a self-supervised speech pre-training method called TERA, which stands for
Transformer Encoder Representations from Alteration. Recent approaches often learn by …

We propose vq-wav2vec to learn discrete representations of audio segments through a
wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel …

We explore unsupervised pre-training for speech recognition by learning representations of
raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting …

The goal of this work is to recognise phrases and sentences being spoken by a talking face,
with or without the audio. Unlike previous works that have focussed on recognising a limited …

Deep learning is progressively gaining popularity as a viable alternative to i-vectors for
speaker recognition. Promising results have been recently obtained with Convolutional …

Mel-filterbanks are fixed, engineered audio features which emulate human perception and
have been used through the history of audio understanding up to today. However, their …

Deep learning is currently playing a crucial role toward higher levels of artificial intelligence.
This paradigm allows neural networks to learn complex and abstract representations, that …