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

This research introduces an enhanced version of the multi-objective speech assessment
model–MOSA-Net+, by leveraging the acoustic features from Whisper, a large-scaled …

Recently, researchers have shown an increasing interest in automatically predicting the
subjective evaluation for speech synthesis systems. This prediction is a challenging task …

Automatic Mean Opinion Score (MOS) prediction is crucial to evaluate the perceptual quality
of the synthetic speech. While recent approaches using pre-trained self-supervised learning …

Because a reference signal is often unavailable in real-world scenarios, reference-free
speech quality and intelligibility assessment models are important for many speech …

Wideband codecs such as AMR-WB or EVS are widely used in (mobile) speech
communication. Evaluation of coded speech quality is often performed subjectively by an …

IEEE Automatic Mean Opinion Score (MOS) prediction is employed to evaluate the quality of
synthetic speech. This study extends the application of predicted MOS to the task of Fake …

In this paper, we propose the speech quality assessment transformer utilizing listener
dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was …

Current state-of-the-art methods for automatic synthetic speech evaluation are based on
MOS prediction neural models. Such MOS prediction models include MOSNet and LDNet …

In speech generation tasks, human subjective ratings, usually referred to as the opinion
score, are considered the" gold standard" for speech quality evaluation, with the mean …