Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Self-supervised speech representation models have succeeded in various tasks, but
improving them for content-related problems using unlabeled data is challenging. We …

Despite improvements to the generalization performance of automated speech recognition
(ASR) models, specializing ASR models for downstream tasks remains a challenging task …

Deep neural networks have demonstrated their capacity in extracting features from speech
inputs. However, these features may include non-linguistic speech factors such as timbre …

Speech-to-text translation (ST) is a cross-modal task that involves converting spoken
language into text in a different language. Previous research primarily focused on …

This paper introduces Robust Spin (R-Spin), a data-efficient domain-specific self-
supervision method for speaker and noise-invariant speech representations by learning …

Despite success across various tasks, self-supervised speech models face significant
challenges in enhancing content-related performance with unlabeled data, requiring …

Recent advances in machine learning have demonstrated that multi-modal pre-training can
improve automatic speech recognition (ASR) performance compared to randomly initialized …

While word error rates of automatic speech recognition (ASR) systems have consistently
fallen, natural language understanding (NLU) applications built on top of ASR systems still …

If you ask a human to describe an image, they might do so in a thousand different ways.
Each of these descriptions depends not only on the image but also on a rich tapestry of …