Significant research has been conducted during the last decade on the application of
machine learning for speech processing, particularly speech recognition. However, in recent …

We propose the (): S emantically-A ligned M ultimodal U tterance-level Cross-L ingual S
peech R epresentation learning framework. Unlike previous works on speech representation …

Prosody is closely linked to the identity of a speaker, leading to individual pitch and
intonation patterns. Therefore, it is challenging in speaker anonymization to generate …

In order to protect the privacy of speech data, speaker anonymization aims for hiding the
identity of a speaker by changing the voice in speech recordings. This typically comes with a …

Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence,
which significantly reduces the inference speed at the cost of accuracy drop compared to …

We investigate the performance of self-supervised pretraining frameworks on pathological
speech datasets used for automatic speech recognition (ASR). Modern end-to-end models …

In this work, we propose a speaker anonymization pipeline that leverages high quality
automatic speech recognition and synthesis systems to generate speech conditioned on …

A high-performance versatile computer-assisted pronunciation training (CAPT) system that
provides the learner immediate feedback as to whether their pronunciation is correct is very …

Noise robustness is essential for deploying automatic speech recognition (ASR) systems in
real-world environments. One way to reduce the effect of noise interference is to employ a …

Transfer learning is a common method to improve the performance of the model on a target
task via pre-training the model on pretext tasks. Different from the methods using …