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Found 11 papers, 5 papers with code
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model
To overcome this limitation, we propose a deep learning-based non-intrusive speech intelligibility assessment model, namely STOI-Net.
A Study on Incorporating Whisper for Robust Speech Assessment
The first part of this study investigates the correlation between the embedding features of Whisper and two self-supervised learning (SSL) models with subjective quality and intelligibility scores.
HAAQI-Net: A non-intrusive neural music quality assessment model for hearing aids
This paper introduces HAAQI-Net, a non-intrusive deep learning model for music quality assessment tailored to hearing aid users.
Speech Enhancement with Zero-Shot Model Selection
Experimental results confirmed that the proposed ZMOS approach can achieve better performance in both seen and unseen noise types compared to the baseline systems and other model selection systems, which indicates the effectiveness of the proposed approach in providing robust SE performance.
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
The DSDT is built based on a prior knowledge of speech and noisy conditions (the speaker, environment, and signal factors are considered in this paper), where each component of the multi-branched encoder performs a particular mapping from noisy to clean speech along the branch in the DSDT.
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications.
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention.
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users.
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model
This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net.
Utilizing Whisper to Enhance Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
Automated assessment of speech intelligibility in hearing aid (HA) devices is of great importance.
