Recent research shows a big convergence in model architecture, training objectives, and
inference methods across various tasks for different modalities. In this paper, we propose …

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural
language processing models, we propose a unified-modal SpeechT5 framework that …

In this paper, we present an update to the NISQA speech quality prediction model that is
focused on distortions that occur in communication networks. In contrast to the previous …

We present the first edition of the VoiceMOS Challenge, a scientific event that aims to
promote the study of automatic prediction of the mean opinion score (MOS) of synthetic …

This paper reports on the second GENEA Challenge to benchmark data-driven automatic co-
speech gesture generation. Participating teams used the same speech and motion dataset …

Co-speech gestures, gestures that accompany speech, play an important role in human
communication. Automatic co-speech gesture generation is thus a key enabling technology …

An effective approach to automatically predict the subjective rating for synthetic speech is to
train on a listening test dataset with human-annotated scores. Although each speech sample …

Evaluating synthetic speech generated by machines is a complicated process, as it involves
judging along multiple dimensions including naturalness, intelligibility, and whether the …

Large language models (LLMs) have made significant advancements in natural language
processing and are concurrently extending the language ability to other modalities, such as …

With the rapid development of deep learning techniques, the generation and counterfeiting
of multimedia material has become increasingly simple. Current technology enables the …