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 …

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 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 …

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

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 …

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 …

We propose a simple and effective cross-lingual transfer learning method to adapt
monolingual wav2vec-2.0 models for Automatic Speech Recognition (ASR) in resource …

With the surge of online meetings, it has become more critical than ever to provide high-
quality speech audio and live captioning under various noise conditions. However, most …

In this paper, we construct a new Japanese speech corpus called" JTubeSpeech." Although
recent end-to-end learning requires large-size speech corpora, open-sourced such corpora …