Search Results for author:
Found 18 papers, 5 papers with code
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization
Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning.
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization
In this work, we introduce EQ-Net: the first holistic framework that solves both the tasks of log-likelihood ratio (LLR) estimation and quantization using a data-driven method.
Speech Recognition using EEG signals recorded using dry electrodes
In this paper, we demonstrate speech recognition using electroencephalography (EEG) signals obtained using dry electrodes on a limited English vocabulary consisting of three vowels and one word using a deep learning model.
Robust Face Verification via Disentangled Representations
Our architecture uses a contrastive loss termand a disentangled generative model to sample negative pairs.
Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design
In all, in this study, we show that the new QM-SP-ML model represents a powerful technique for molecular forward design.
Chemical Physics Materials Science Computational Physics Quantum Physics
Generating EEG features from Acoustic features
In this paper we demonstrate predicting electroencephalograpgy (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN).
EEG based Continuous Speech Recognition using Transformers
In this paper we investigate continuous speech recognition using electroencephalography (EEG) features using recently introduced end-to-end transformer based automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Continuous Speech Recognition using EEG and Video
In this paper we investigate whether electroencephalography (EEG) features can be used to improve the performance of continuous visual speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving EEG based Continuous Speech Recognition
In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Voice Activity Detection in presence of background noise using EEG
In this paper we demonstrate that performance of voice activity detection (VAD) system operating in presence of background noise can be improved by concatenating acoustic input features with electroencephalography (EEG) features.
Sound Audio and Speech Processing Signal Processing
Spoken Speech Enhancement using EEG
In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model, gated recurrent unit (GRU) regression based model, temporal convolutional network (TCN) regression model and finally using a mixed TCN GRU regression model.
State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG
In this paper we first demonstrate continuous noisy speech recognition using electroencephalography (EEG) signals on English vocabulary using different types of state of the art end-to-end automatic speech recognition (ASR) models, we further provide results obtained using EEG data recorded under different experimental conditions.
Audio and Speech Processing Sound
Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation
In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage is introduced.
Advancing Speech Recognition With No Speech Or With Noisy Speech
In this paper we demonstrate end-to-end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Robust End-to-End Speaker Verification Using EEG
In this paper we demonstrate that performance of a speaker verification system can be improved by concatenating electroencephalography (EEG) signal features with speech signal features or only using EEG signal features.
Speech Recognition With No Speech Or With Noisy Speech Beyond English
In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limited joint English and Chinese vocabulary using EEG features with no speech signal as input.
Deep Log-Likelihood Ratio Quantization
In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting.
Speech Recognition with no speech or with noisy speech
The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
