Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

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 …

Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been
extensively investigated in the supervised setting. This assumes the existence of a parallel …

Communication has been an essential part of being human and living in society. There are
several different languages and variations of them, so you can speak English in one place …

Speech processing for under-resourced languages is an active field of research, which has
experienced significant progress during the past decade. We propose, in this paper, a …

We introduce a new collection of spoken English audio suitable for training speech
recognition systems under limited or no supervision. It is derived from open-source audio …

End-to-end automatic speech recognition (ASR) can significantly reduce the burden of
developing ASR systems for new languages, by eliminating the need for linguistic …

In this paper, we propose a simple yet effective framework for multilingual end-to-end
speech translation (ST), in which speech utterances in source languages are directly …

Recently there has been interest in the approaches for training speech recognition systems
for languages with limited resources. Under the IARPA Babel program such resources have …

Techniques for multi-lingual and cross-lingual speech recognition can help in low resource
scenarios, to bootstrap systems and enable analysis of new languages and domains. End-to …