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Found 11 papers, 3 papers with code
Audio-Visual Speech Enhancement and Separation by Leveraging Multi-Modal Self-Supervised Embeddings
In summary, our results confirm the effectiveness of our proposed model for the AVSS task with proper fine-tuning strategies, demonstrating that multi-modal self-supervised embeddings obtained from AV-HUBERT can be generalized to audio-visual regression tasks.
Boosting Self-Supervised Embeddings for Speech Enhancement
We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.
Ranked #6 on
Speech Enhancement
on DEMAND
Partially Fake Audio Detection by Self-attention-based Fake Span Discovery
Also ADD 2022 is the first challenge to propose the partially fake audio detection task.
MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech
Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean speech are required during training.
Speech Recovery for Real-World Self-powered Intermittent Devices
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.
EMA2S: An End-to-End Multimodal Articulatory-to-Speech System
Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments.
Deep Learning Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems
However, the performance of FD systems is diminished owing to low-resolution (LR) accelerometer signals.
A Study of Incorporating Articulatory Movement Information in Speech Enhancement
Although deep learning algorithms are widely used for improving speech enhancement (SE) performance, the performance remains limited under highly challenging conditions, such as unseen noise or noise signals having low signal-to-noise ratios (SNRs).
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.
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).
