15 papers
Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement
Szu-Wei Fu, Rong Chao, Xuesong Yang +6
Universal Speech Enhancement (USE) aims to restore speech quality under diverse degradation conditions while preserving signal fidelity. Despite recent progress, key challenges in…
Few-Shot and Pseudo-Label Guided Speech Quality Evaluation with Large Language Models
Ryandhimas E. Zezario, Dyah A. M. G. Wisnu, Szu-Wei Fu +3
In this paper, we introduce GatherMOS, a novel framework that leverages large language models (LLM) as meta-evaluators to aggregate diverse signals into quality predictions. Gather…
MOS-Bias: From Hidden Gender Bias to Gender-Aware Speech Quality Assessment
Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +5
The Mean Opinion Score (MOS) serves as the standard metric for speech quality assessment, yet biases in human annotations remain underexplored. We conduct the first systematic anal…
STSM-FiLM: A FiLM-Conditioned Neural Architecture for Time-Scale Modification of Speech
Dyah A. M. G. Wisnu, Ryandhimas E. Zezario, Stefano Rini +4
Time-Scale Modification (TSM) of speech aims to alter the playback rate of audio without changing its pitch. While classical methods like Waveform Similarity-based Overlap-Add (WSO…
Speech Intelligibility Assessment with Uncertainty-Aware Whisper Embeddings and sLSTM
Ryandhimas E. Zezario, Dyah A. M. G. Wisnu, Hsin-Min Wang +1
Non-intrusive speech intelligibility prediction remains challenging due to variability in speakers, noise conditions, and subjective perception. We propose an uncertainty-aware app…
Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings
Dyah A. M. G. Wisnu, Ryandhimas E. Zezario, Stefano Rini +2
We present a system for automatic multi-axis perceptual quality prediction of generative audio, developed for Track 2 of the AudioMOS Challenge 2025. The task is to predict four Au…