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

This paper presents XLSR which learns cross-lingual speech representations by pretraining
a single model from the raw waveform of speech in multiple languages. We build on …

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 …

Machine learning has been advancing dramatically over the past decade. Most strides are
human-based applications due to the availability of large-scale datasets; however …

Unsupervised pre-training is now the predominant approach for both text and speech
understanding. Self-attention models pre-trained on large amounts of unannotated data …

In this paper, we explore vector quantization for acoustic unit discovery. Leveraging
unlabelled data, we aim to learn discrete representations of speech that separate phonetic …

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 …

We present the Zero Resource Speech Challenge 2019, which proposes to build a speech
synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without …

In this paper, we present a method for learning discrete linguistic units by incorporating
vector quantization layers into neural models of visually grounded speech. We show that our …