303 citations · 612 across the 62 of their papers we have counts for
12 papers · 1 filter
RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation
Rong Chao, Sung-Feng Huang, Moreno La Quatra +4
We present RT-SEMamba, a fully causal speech enhancement (SE) model built upon causal time-frequency Mamba blocks. Unlike Transformer-based architectures that rely on a growing key…
HDA-SELD: Hierarchical Cross-Modal Distillation with Multi-Level Data Augmentation for Low-Resource Audio-Visual Sound Event Localization and Detection
Qing Wang, Ya Jiang, Hang Chen +3
This work presents HDA-SELD, a unified framework that combines hierarchical cross-modal distillation (HCMD) and multi-level data augmentation to address low-resource audio-visual (…
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition
Ming Gao, Shilong Wu, Hang Chen +6
Meetings are a valuable yet challenging scenario for speech applications due to complex acoustic conditions. This paper summarizes the outcomes of the MISP 2025 Challenge, hosted a…
Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations
Whenty Ariyanti, Kuan-Yu Chen, Sabato Marco Siniscalchi +2
Perceptual voice quality assessment plays a vital role in diagnosing and monitoring voice disorders. Traditional methods, such as the Consensus Auditory-Perceptual Evaluation of Vo…
Exploiting Consistency-Preserving Loss and Perceptual Contrast Stretching to Boost SSL-based Speech Enhancement
Muhammad Salman Khan, Moreno La Quatra, Kuo-Hsuan Hung +3
Self-supervised representation learning (SSL) has attained SOTA results on several downstream speech tasks, but SSL-based speech enhancement (SE) solutions still lag behind. To add…
An Investigation of Incorporating Mamba for Speech Enhancement
Rong Chao, Wen-Huang Cheng, Moreno La Quatra +4
This work aims to investigate the use of a recently proposed, attention-free, scalable state-space model (SSM), Mamba, for the speech enhancement (SE) task. In particular, we emplo…
Differentially Private Adapters for Parameter Efficient Acoustic Modeling
Chun-Wei Ho, Chao-Han Huck Yang, Sabato Marco Siniscalchi
In this work, we devise a parameter-efficient solution to bring differential privacy (DP) guarantees into adaptation of a cross-lingual speech classifier. We investigate a new froz…
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition
Chao-Han Huck Yang, I-Fan Chen, Andreas Stolcke +2
Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise pertu…
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
Hao Yen, Chao-Han Huck Yang, Hu Hu +9
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC…
PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification
Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee
We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring di…
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convol…
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +4
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolu…