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eess.AS2024
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).…
eess.AS2024
Language-Universal Speech Attributes Modeling for Zero-Shot Multilingual Spoken Keyword Recognition
Hao Yen, Pin-Jui Ku, Sabato Marco Siniscalchi +1
We propose a novel language-universal approach to end-to-end automatic spoken keyword recognition (SKR) leveraging upon (i) a self-supervised pre-trained model, and (ii) a set of u…
eess.AS2024
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori
Hu Hu, Sabato Marco Siniscalchi, Chin-Hui Lee
In this work, we aim to establish a Bayesian adaptive learning framework by focusing on estimating latent variables in deep neural network (DNN) models. Latent variables indeed enc…