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

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

Speech quality estimation has recently undergone a paradigm shift from human-hearing
expert designs to machine-learning models. However, current models rely mainly on …

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 …

Convolutional neural networks (CNNs) have been widely utilized as the main building block
for many non-intrusive speech quality assessment (NISQA) methods. A new trend is to add a …

This paper introduces a novel objective function for quality mean opinion score (MOS)
prediction of unseen speech synthesis systems. The proposed function measures the …

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

We present MooseNet, a trainable speech metric that predicts the listeners' Mean Opinion
Score (MOS). We propose a novel approach where the Probabilistic Linear Discriminative …

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 …

In this paper, we evaluate feature extraction models for predicting speech quality. We also
propose a model architecture to compare embeddings of supervised learning and self …