15 citations · 50 across the 25 of their papers we have counts for
25 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…
A Study on Zero-Shot Non-Intrusive Speech Intelligibility for Hearing Aids Using Large Language Models
Ryandhimas E. Zezario, Dyah A. M. G. Wisnu, Hsin-Min Wang +1
This work focuses on zero-shot non-intrusive speech assessment for hearing aids (HA) using large language models (LLMs). Specifically, we introduce GPT-Whisper-HA, an extension of…
Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges
Ryandhimas E. Zezario
This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature ex…
Feature Importance across Domains for Improving Non-Intrusive Speech Intelligibility Prediction in Hearing Aids
Ryandhimas E. Zezario, Sabato M. Siniscalchi, Fei Chen +2
Given the critical role of non-intrusive speech intelligibility assessment in hearing aids (HA), this paper enhances its performance by introducing Feature Importance across Domain…
HighRateMOS: Sampling-Rate Aware Modeling for Speech Quality Assessment
Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +9
Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When faced with higher-rate audio at test time, these models can produce bi…
A Study on Speech Assessment with Visual Cues
Shafique Ahmed, Ryandhimas E. Zezario, Nasir Saleem +3
Non-intrusive assessment of speech quality and intelligibility is essential when clean reference signals are unavailable. In this work, we propose a multimodal framework that integ…
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids
Shafique Ahmed, Ryandhimas E. Zezario, Hui-Guan Yuan +4
The prevalence of hearing aids is increasing. However, optimizing the amplification processes of hearing aids remains challenging due to the complexity of integrating multiple modu…
The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction
Wen-Chin Huang, Szu-Wei Fu, Erica Cooper +5
We present the third edition of the VoiceMOS Challenge, a scientific initiative designed to advance research into automatic prediction of human speech ratings. There were three tra…
A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models
Ryandhimas E. Zezario, Sabato M. Siniscalchi, Hsin-Min Wang +1
This work investigates two strategies for zero-shot non-intrusive speech assessment leveraging large language models. First, we explore the audio analysis capabilities of GPT-4o. S…
HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids
Dyah A. M. G. Wisnu, Stefano Rini, Ryandhimas E. Zezario +2
This paper introduces HAAQI-Net, a non-intrusive deep learning-based music audio quality assessment model for hearing aid users. Unlike traditional methods like the Hearing Aid Aud…
A Study on Incorporating Whisper for Robust Speech Assessment
Ryandhimas E. Zezario, Yu-Wen Chen, Szu-Wei Fu +3
This research introduces an enhanced version of the multi-objective speech assessment model--MOSA-Net+, by leveraging the acoustic features from Whisper, a large-scaled weakly supe…
Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata
Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh +2
Automated speech intelligibility assessment is pivotal for hearing aid (HA) development. In this paper, we present three novel methods to improve intelligibility prediction accurac…
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model
Ryandhimas E. Zezario, Bo-Ren Brian Bai, Chiou-Shann Fuh +2
This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net. MPL consists of two stages: obtaining pseudo-label…
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Ryandhimas E. Zezario, Szu-wei Fu, Fei Chen +3
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention. Many studies report that these DL-based models yield satisfactory assessme…
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh +2
Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a me…
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen +3
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. Experimental…
Speech Enhancement with Zero-Shot Model Selection
Ryandhimas E. Zezario, Chiou-Shann Fuh, Hsin-Min Wang +1
Recent research on speech enhancement (SE) has seen the emergence of deep-learning-based methods. It is still a challenging task to determine the effective ways to increase the gen…
STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model
Ryandhimas E. Zezario, Szu-Wei Fu, Chiou-Shann Fuh +2
The calculation of most objective speech intelligibility assessment metrics requires clean speech as a reference. Such a requirement may limit the applicability of these metrics in…
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh +9
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our st…