9 papers
A Knowledge-Driven Approach to Target Speech Extraction in the Presence of Background Sound Effects for Cinematic Audio Source Separation (CASS)
Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1
We propose a knowledge-driven approach to speech target extraction in the presence of background sound effects already recorded in cinematic audio. The specific knowledge sources s…
A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation
Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1
We propose a knowledge-driven, model-based approach to segmenting audio into single-category and mixed-category chunks with applications to source separation. "Knowledge" here deno…
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).…
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…
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…
Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints
Hao Yen, Sabato Marco Siniscalchi, Chin-Hui Lee
We propose a first step toward multilingual end-to-end automatic speech recognition (ASR) by integrating knowledge about speech articulators. The key idea is to leverage a rich set…