activity
20232025
most citedNoise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

An Event-triggered System for Social Persuasion and Danger Alert in Elder Home Monitoring

Jun-Yi Liu, Chung-Hao Chen, Ya-Chi Tsao +3

In the study, the physical state and mental state of elders are both considered, and an event-triggered system has developed to detect events: watch dog, danger notice and photo li…

cs.SD2025

Leveraging Mamba with Full-Face Vision for Audio-Visual Speech Enhancement

Rong Chao, Wenze Ren, You-Jin Li +5

Recent Mamba-based models have shown promise in speech enhancement by efficiently modeling long-range temporal dependencies. However, models like Speech Enhancement Mamba (SEMamba)…

cs.SD2025

Universal Speech Enhancement with Regression and Generative Mamba

Rong Chao, Rauf Nasretdinov, Yu-Chiang Frank Wang +3

The Interspeech 2025 URGENT Challenge aimed to advance universal, robust, and generalizable speech enhancement by unifying speech enhancement tasks across a wide variety of conditi…

eess.AS2024

Robust Audio-Visual Speech Enhancement: Correcting Misassignments in Complex Environments with Advanced Post-Processing

Wenze Ren, Kuo-Hsuan Hung, Rong Chao +3

This paper addresses the prevalent issue of incorrect speech output in audio-visual speech enhancement (AVSE) systems, which is often caused by poor video quality and mismatched tr…

eess.AS2024

MC-SEMamba: A Simple Multi-channel Extension of SEMamba

Wen-Yuan Ting, Wenze Ren, Rong Chao +3

Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a prom…

eess.AS2023★ 1 cited

Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement

Yu-Wen Chen, Julia Hirschberg, Yu Tsao

Speech emotion recognition (SER) often experiences reduced performance due to background noise. In addition, making a prediction on signals with only background noise could undermi…