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Found 14 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 Using Self-supervised Learning to Improve Speech Intelligibility in Cochlear Implant Simulations
The aim of this study is to explore the effectiveness of audio-visual speech enhancement (AVSE) in enhancing the intelligibility of vocoded speech in cochlear implant (CI) simulations.
A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids
In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology.
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 Robust Real-time Audio-Visual Speech Enhancement
The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing.
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.
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
Most recent works on sentiment analysis have exploited the text modality.
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare
The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state.
A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks
When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification.
AV Speech Enhancement Challenge using a Real Noisy Corpus
This paper presents, a first of its kind, audio-visual (AV) speech enhacement challenge in real-noisy settings.
CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement
In addition, our work challenges a popular belief that a scarcity of multi-language large vocabulary AV corpus and wide variety of noises is a major bottleneck to build a robust language, speaker and noise independent SE systems.
Exploiting Deep Learning for Persian Sentiment Analysis
The rise of social media is enabling people to freely express their opinions about products and services.
