Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model

arXiv:2308.09262v3 Announce Type: replace-cross
Abstract: This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net. MPL consists of two stages: obtaining pseudo-label scores from a pretrained model and performing multi-task learning. The 3QUEST metrics, namely Speech-MOS (S-MOS), Noise-MOS (N-MOS), and General-MOS (G-MOS), are the assessment targets. The pretrained MOSA-Net model is utilized to estimate three pseudo labels: perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI). Multi-task learning is …

abstract arxiv assessment cs.lg cs.sd eess.as general metrics mos multi-task learning noise quality speech study targets type