From the 1 of 10 linked papers with an AI index.
10 papers
LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement
Chih-Ning Chen, Jen-Cheng Hou, Hsin-Min Wang +3
The paper introduces a reinforcement learning framework for audio‑visual speech enhancement that uses a large language model to generate natural‑language feedback, which is convert…
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…
SAVe: Self-Supervised Audio-visual Deepfake Detection Exploiting Visual Artifacts and Audio-visual Misalignment
Sahibzada Adil Shahzad, Ammarah Hashmi, Junichi Yamagishi +5
Multimodal deepfakes can exhibit subtle visual artifacts and cross-modal inconsistencies, which remain challenging to detect, especially when detectors are trained primarily on cur…
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…