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Found 16 papers, 5 papers with code
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).
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.
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.
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).
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.
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.
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.
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.
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.
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 #9 on
Speech Enhancement
on VoiceBank + DEMAND
Audio-Visual Speech Enhancement and Separation by Utilizing 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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Interpretations of Domain Adaptations via Layer Variational Analysis
The method shows an increase in efficiency and accuracy for domain adaptation.
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection
This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event.
Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning
Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain.
Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility
Moreover, new objective measures are proposed that combine current objective measures using deep learning techniques to predict subjective quality and intelligibility.
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech
To improve the robustness of the encoder for SE, a novel self-distillation mechanism combined with adversarial training is introduced.
