most citedSTOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model

15 citations · 15 across the 2 of their papers we have counts for

collaborators

5 papers

cs.SD2024

The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction

Wen-Chin Huang, Szu-Wei Fu, Erica Cooper +5

We present the third edition of the VoiceMOS Challenge, a scientific initiative designed to advance research into automatic prediction of human speech ratings. There were three tra…

eess.AS2020

Speech Enhancement with Zero-Shot Model Selection

Ryandhimas E. Zezario, Chiou-Shann Fuh, Hsin-Min Wang +1

Recent research on speech enhancement (SE) has seen the emergence of deep-learning-based methods. It is still a challenging task to determine the effective ways to increase the gen…

cs.SD202015 cited

STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model

Ryandhimas E. Zezario, Szu-Wei Fu, Chiou-Shann Fuh +2

The calculation of most objective speech intelligibility assessment metrics requires clean speech as a reference. Such a requirement may limit the applicability of these metrics in…

eess.AS2020

Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing

Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh +9

The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our st…

eess.AS2020

Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders

Cheng Yu, Ryandhimas E. Zezario, Syu-Siang Wang +5

Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model gener…