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 …

Contrastive Predictive Coding (CPC), based on predicting future segments of speech from
past segments is emerging as a powerful algorithm for representation learning of speech …

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 …

Spectacular progress in the information processing sciences (machine learning, wearable
sensors) promises to revolutionize the study of cognitive development. Here, we analyse the …

Abstract The Interspeech 2015 Zero Resource Speech Challenge aims at discovering
subword and word units from raw speech. The challenge provides the first unified and open …

We investigate the problem of acoustic modeling in which prior language-specific
knowledge and transcribed data are unavailable. We present an unsupervised model that …

This book provides an extensive overview of research into child production and perception.
It focuses primarily on the first two years of life because, for the majority of children, that …

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 …

We present a new framework for the evaluation of speech rep-resentations in zero-resource
settings, that extends and complements previous work by Carlin, Jansen and Hermansky [1] …

Zero-resource speech technology is a growing research area that aims to develop methods
for speech processing in the absence of transcriptions, lexicons, or language modelling text …