3 accepted papers
This study addresses the task of speech quality assessment (SQA), which aims to automatically predict the subjective quality of a given speech. Recent efforts have focused on training neural-based models to predict the mean opinion score (MOS) of speech samples produced by text-to-speech or voice co…
The automatic speech quality assessment (SQA) has been extensively studied to predict the speech quality without time-consuming questionnaires. Recently, neural-based SQA models have been actively developed for speech samples produced by text-to-speech or voice conversion, with a primary focus on tr…
Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one r…