Search Results for author:
Found 17 papers, 1 papers with code
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.
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 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 Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms.
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.
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.
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.
Contextual Audio-Visual Switching For Speech Enhancement in Real-World Environments
In this paper, we introduce a novel contextual AV switching component that contextually exploits AV cues with respect to different operating conditions to estimate clean audio, without requiring any SNR estimation.
Exploiting Deep Learning for Persian Sentiment Analysis
The rise of social media is enabling people to freely express their opinions about products and services.
Lip-Reading Driven Deep Learning Approach for Speech Enhancement
The proposed audio-visual (AV) speech enhancement framework operates at two levels.
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation
The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises.
