We consider the task of unsupervised extraction of meaningful latent representations of
speech by applying autoencoding neural networks to speech waveforms. The goal is to …

We describe a new challenge aimed at discovering subword and word units from raw
speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It …

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 …

Recent progress in self-supervised or unsupervised machine learning has opened the
possibility of building a full speech processing system from raw audio without using any …

Unsupervised representation learning for speech processing has matured greatly in the last
few years. Work in computer vision and natural language processing has paved the way, but …

The success of supervised deep neural networks (DNNs) in speech recognition cannot be
transferred to zero-resource languages where the requisite transcriptions are unavailable …

We adopt a Dirichlet process Gaussian mixture model (DPGMM) for unsupervised acoustic
modeling and represent speech frames with Gaussian posteriorgrams. The model performs …

This paper reports on the results of the Zero Resource Speech Challenge 2015, the first
unified benchmark for zero resource speech technology, which aims at the unsupervised …

Recent work has demonstrated, on small datasets, the feasibility of jointly learning
specialized speaker and phone embeddings, in a weakly supervised siamese DNN …

Recent work has explored deep architectures for learning acoustic features in an
unsupervised or weakly-supervised way for phone recognition. Here we investigate the role …