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 …

Despite the growing interest in unsupervised learning, extracting meaningful knowledge
from unlabelled audio remains an open challenge. To take a step in this direction, we …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The
2014 evaluation offered multiple tracks on lecture transcription and translation based on the …

This paper proposes a simple yet effective model-based neural network speaker adaptation
technique that learns speaker-specific hidden unit contributions given adaptation data …

This work presents a broad study on the adaptation of neural network acoustic models by
means of learning hidden unit contributions (LHUC)-a method that linearly re-combines …

Deep neural networks (DNNs) have gained remarkable success in speech recognition,
partially attributed to the flexibility of DNN models in learning complex patterns of speech …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

This paper introduces deep neural network (DNN)–hidden Markov model (HMM)-based
methods to tackle speech recognition in heterogeneous groups of speakers including …

Children's speech poses challenges to speech recognition due to strong age-dependent
anatomical variations and a lack of large, publicly-available corpora. In this paper we …