We explore unsupervised pre-training for speech recognition by learning representations of
raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting …

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs),
where misclassification rules are hidden inside normal models, only to be triggered by very …

The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …

Whereas conventional spoken language understanding (SLU) systems map speech to text,
and then text to intent, end-to-end SLU systems map speech directly to intent through a …

Deep generative models, which target reproducing the data distribution to produce novel
images, have made unprecedented advancements in recent years. However, one critical …

Affective computing is an emerging area of research that aims to enable intelligent systems
to recognize, feel, infer and interpret human emotions. The widely spread online and off-line …

In this paper, we propose a unified pre-training approach called UniSpeech to learn speech
representations with both labeled and unlabeled data, in which supervised phonetic CTC …

Is pushing numbers on a single benchmark valuable in automatic speech recognition?
Research results in acoustic modeling are typically evaluated based on performance on a …

Transfer learning is a powerful approach that allows users to quickly build accurate deep-
learning (Student) models by" learning" from centralized (Teacher) models pretrained with …

The primary aim of this study was to contribute to the development of multilingual automatic
speech recognition for lower-resourced Turkic languages. Ten languages—Azerbaijani …