Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …

Despite rapid progress in the recent past, current speech recognition systems still require
labeled training data which limits this technology to a small fraction of the languages spoken …

Abstract We introduce Generative Spoken Language Modeling, the task of learning the
acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and …

We propose using self-supervised discrete representations for the task of speech
resynthesis. To generate disentangled representation, we separately extract low-bitrate …

One-shot voice conversion (VC), which performs conversion across arbitrary speakers with
only a single target-speaker utterance for reference, can be effectively achieved by speech …

The goal of voice conversion is to transform source speech into a target voice, keeping the
content unchanged. In this paper, we focus on self-supervised representation learning for …

Deep generative models can generate high-fidelity audio conditioned on varioustypes of
representations (eg, mel-spectrograms, Mel-frequency Cepstral Coefficients (MFCC)) …

Models that predict brain responses to stimuli provide one measure of understanding of a
sensory system and have many potential applications in science and engineering. Deep …

One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned
discrete representation uses only a fraction of the full capacity of the codebook, also known …