Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …

Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the
performance of a primary task. A common usage is to manually select multiple auxiliary …

Emotional expressions form a key part of user behavior on today's digital platforms. While
multimodal emotion recognition techniques are gaining research attention, there is a lack of …

Recurrent neural network language models (RNNLMs) generally outperform n-gram
language models when used in automatic speech recognition (ASR). Adapting RNNLMs to …

We propose strategies for a state-of-the-art keyword search (KWS) system developed by the
SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation …

Spoken term detection (STD) aims at retrieving data from a speech repository given a textual
representation of the search term. Nowadays, it is receiving much interest due to the large …

The performance of neural anti-spoofing models has rapidly improved in recent years due to
larger network architectures and better training methodologies. However, these systems …

Self-supervised contrastive learning offers a means of learning informative features from a
pool of unlabeled data. In this paper, we investigate another useful approach. We propose …

In this paper, we propose a cross-lingual deep neural network (DNN) based submodular
unbiased data selection approach for low-resource keyword search (KWS). A small amount …

Deep Neural Networks (DNNs) trained on multilingual data have proven useful for improving
speech recognition in languages with limited resources. In this framework, data from rich …