4 papers
A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR
Hao Yen, Pin-Jui Ku, Sabato Marco Siniscalchi +1
We propose a bottom-up framework for automatic speech recognition (ASR) in syllable-based languages by unifying language-universal articulatory attribute modeling with syllable-lev…
An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement
Chun-Wei Ho, Pin-Jui Ku, Hao Yen +3
We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core id…
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer
Hu Hu, Sabato Marco Siniscalchi, Chao-Han Huck Yang +1
In this work, we propose a novel variational Bayesian adaptive learning approach for cross-domain knowledge transfer to address acoustic mismatches between training and testing con…
An Explicit Consistency-Preserving Loss Function for Phase Reconstruction and Speech Enhancement
Pin-Jui Ku, Chun-Wei Ho, Hao Yen +2
In this work, we propose a novel consistency-preserving loss function for recovering the phase information in the context of phase reconstruction (PR) and speech enhancement (SE).…