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Found 6 papers, 1 papers with code
Audio-Visual Speech Enhancement in Noisy Environments via Emotion-Based Contextual Cues
By integrating emotional features, the proposed system achieves significant improvements in both objective and subjective assessments of speech quality and intelligibility, especially in challenging noise environments.
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
A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals.
A Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning for Hearing-Assistive Technologies
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are generally trained to minimise the distance between clean and enhanced speech features.
A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement
To address this problem, we propose to integrate a novel temporal attentive-pooling (TAP) mechanism into a conventional convolutional recurrent neural network, termed as TAP-CRNN.
Towards Intelligibility-Oriented Audio-Visual Speech Enhancement
To the best of our knowledge, this is the first work that exploits the integration of AV modalities with an I-O based loss function for SE.
