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Found 16 papers, 3 papers with code
Unsupervised Face-Masked Speech Enhancement Using Generative Adversarial Networks With Human-in-the-Loop Assessment Metrics
(2) The integration of the MaskQSS predictor enhanced the ability of HL-StarGAN to transform face mask voices into high-quality speech; this enhancement is evident in both objective and subjective tests, outperforming conventional StarGAN and CycleGAN-based systems.
IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays
Beamforming techniques are popular in speech-related applications due to their effective spatial filtering capabilities.
Continuous Speech for Improved Learning Pathological Voice Disorders
The experiments were conducted on a large-scale database, wherein 1, 045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019.
Speech Enhancement Based on Cyclegan with Noise-informed Training
Herein, we investigate a potential limitation of the clean-to-noisy conversion part and propose a novel noise-informed training (NIT) approach to improve the performance of the original CycleGAN SE system.
Speech Enhancement-assisted Voice Conversion in Noisy Environments
Numerous voice conversion (VC) techniques have been proposed for the conversion of voices among different speakers.
Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario
In this study, we propose an attention-based MTL (ATM) approach that integrates MTL and the attention-weighting mechanism to simultaneously realize a multi-model learning structure that performs speech enhancement (SE) and speaker identification (SI).
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application
The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users.
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.
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder
For speech-related applications in IoT environments, identifying effective methods to handle interference noises and compress the amount of data in transmissions is essential to achieve high-quality services.
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement
Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE).
Distributed Microphone Speech Enhancement based on Deep Learning
Speech-related applications deliver inferior performance in complex noise environments.
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet Transform
The presented DWT-based SE method with various scaling factors for the detail part is evaluated with a subset of Aurora-2 database, and the PESQ metric is used to indicate the quality of processed speech signals.
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer.
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer.
