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Found 13 papers, 6 papers with code
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids
To address this challenge, we present NeuroAMP, a novel deep neural network designed for end-to-end, personalized amplification in hearing aids.
A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models
We evaluate the assessment metrics predicted by GPT-4o and GPT-Whisper, examining their correlation with human-based quality and intelligibility assessments and the character error rate (CER) of automatic speech recognition.
HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids
Experimental results demonstrate HAAQI-Net's effectiveness, achieving a Linear Correlation Coefficient (LCC) of 0. 9368 , a Spearman's Rank Correlation Coefficient (SRCC) of 0. 9486 , and a Mean Squared Error (MSE) of 0. 0064 and inference time significantly reduces from 62. 52 to 2. 54 seconds.
A Study on Incorporating Whisper for Robust Speech Assessment
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 supervised model.
Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata
Automated speech intelligibility assessment is pivotal for hearing aid (HA) development.
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.
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.
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
Experimental results show that MOSA-Net can improve the linear correlation coefficient (LCC) by 0. 026 (0. 990 vs 0. 964 in seen noise environments) and 0. 012 (0. 969 vs 0. 957 in unseen noise environments) in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-task model for PESQ prediction, and improve LCC by 0. 021 (0. 985 vs 0. 964 in seen noise environments) and 0. 047 (0. 836 vs 0. 789 in unseen noise environments) in short-time objective intelligibility (STOI) prediction, compared to STOI-Net (based on CRNN), an existing single-task model for STOI prediction.
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.
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.
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.
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.
