This paper proposes an unsupervised data selection method by using a submodular
function based on contrastive loss ratios of target and training data sets. A model using a …

Negative transfer in training of acoustic models for automatic speech recognition has been
reported in several contexts such as domain change or speaker characteristics. This paper …

Huge amounts of digital videos are being produced and broadcast every day, leading to
giant media archives. Effective techniques are needed to make such data accessible further …

We present novel features designed with a deep neural network for Machine Translation
(MT) Quality Estimation (QE). The features are learned with a Continuous Space Language …

This paper presents a new method for the discovery of latent domains in diverse speech
data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech …

Speech recognition systems are often highly domain dependent, a fact widely reported in
the literature. However the concept of domain is complex and not bound to clear criteria …

This paper introduces the development of ShefCE: a Cantonese-English bilingual speech
corpus from L2 English speakers in Hong Kong. Bilingual parallel recording materials were …

This paper addresses the automatic quality estimation of spoken language translation (SLT).
This relatively new task is defined and formalized as a sequence-labeling problem where …

Spoken language translation (SLT) combines automatic speech recognition (ASR) and
machine translation (MT). During the decoding stage, the best hypothesis produced by the …

A quality estimation (QE) approach informed with machine translation (MT) and speech
recognition (ASR) features has recently shown to improve the performance of a spoken …