Models, code, and papers for "Ahmed H Tewfik":

Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation

Jun 18, 2019
Marius Arvinte, Sriram Vishwanath, Ahmed H. Tewfik

In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage is introduced. We analyze the dependency between the average magnitude of different L-values from the same quadrature amplitude modulation (QAM) symbol and show they follow a consistent ordering. Based on this we design a deep autoencoder that jointly compresses and separately reconstructs each L-value, allowing the use of a weighted loss function that aims to more accurately reconstructs low magnitude inputs. Our method is shown to be competitive with state-of-the-art maximum mutual information quantization schemes, reducing the required memory footprint by a ratio of up to two and a loss of performance smaller than 0.1 dB with less than two effective bits per L-value or smaller than 0.04 dB with 2.25 effective bits. We experimentally show that our proposed method is a universal compression scheme in the sense that after training on an LDPC-coded Rayleigh fading scenario we can reuse the same network without further training on other channel models and codes while preserving the same performance benefits.

* Submitted to IEEE Globecom 2019 

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Deep Log-Likelihood Ratio Quantization

Mar 11, 2019
Marius Arvinte, Ahmed H. Tewfik, Sriram Vishwanath

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. A deep autoencoder network is trained to compress, quantize and reconstruct the bit log-likelihood ratios corresponding to a single transmitted symbol. Specifically, the encoder maps to a latent space with dimension equal to the number of sufficient statistics required to recover the inputs - equal to three in this case - while the decoder aims to reconstruct a noisy version of the latent representation with the purpose of modeling quantization effects in a differentiable way. Simulation results show that, when applied to a standard rate-1/2 low-density parity-check (LDPC) code, a finite precision compression factor of nearly three times is achieved when storing an entire codeword, with an incurred loss of performance lower than 0.1 dB compared to straightforward scalar quantization of the log-likelihood ratios.

* Submitted to EUSIPCO 2019 

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EEG based Continuous Speech Recognition using Transformers

Dec 31, 2019
Gautam Krishna, Co Tran, Mason Carnahan, Ahmed H Tewfik

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. Our results show that transformer based model demonstrate faster inference and training compared to recurrent neural network (RNN) based sequence-to-sequence EEG models but performance of the RNN based models were better than transformer based model during test time on a limited English vocabulary.

* Work in progress for submission to EUSIPCO 2020 

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Continuous Speech Recognition using EEG and Video

Dec 27, 2019
Gautam Krishna, Mason Carnahan, Co Tran, Ahmed H Tewfik

In this paper we investigate whether electroencephalography (EEG) features can be used to improve the performance of continuous visual speech recognition systems. We implemented a connectionist temporal classification (CTC) based end-to-end automatic speech recognition (ASR) model for performing recognition. Our results demonstrate that EEG features are helpful in enhancing the performance of continuous visual speech recognition systems.

* On preparation for submission to EUSIPCO 2020. arXiv admin note: text overlap with arXiv:1911.11610, arXiv:1911.04261 

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Advancing Speech Recognition With No Speech Or With Noisy Speech

Jul 27, 2019
Gautam Krishna, Co Tran, Mason Carnahan, Ahmed H Tewfik

In this paper we demonstrate end to end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input. An attention model based automatic speech recognition (ASR) and connectionist temporal classification (CTC) based ASR systems were implemented for performing recognition. We further demonstrate CSR for noisy speech by fusing with EEG features.

* Accepted for publication at IEEE EUSIPCO 2019. Camera-ready version. arXiv admin note: text overlap with arXiv:1906.08045 

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Speech Recognition with no speech or with noisy speech

Mar 02, 2019
Gautam Krishna, Co Tran, Jianguo Yu, Ahmed H Tewfik

The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of automatic speech recognition systems using EEG features will increase their performance. Finally, we demonstrate the ability to recognize words from EEG with no speech signal on a limited English vocabulary with high accuracy.

* Accepted for ICASSP 2019 

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Improving EEG based Continuous Speech Recognition

Dec 24, 2019
Gautam Krishna, Co Tran, Mason Carnahan, Yan Han, Ahmed H Tewfik

In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems. A connectionist temporal classification (CTC) based automatic speech recognition (ASR) system was implemented for performing recognition. We introduce techniques to initialize the weights of the recurrent layers in the encoder of the CTC model with more meaningful weights rather than with random weights and we make use of an external language model to improve the beam search during decoding time. We finally study the problem of predicting articulatory features from EEG features in this paper.

* On preparation for submission to EUSIPCO 2020. arXiv admin note: text overlap with arXiv:1911.04261, arXiv:1906.08871 

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Spoken Speech Enhancement using EEG

Oct 29, 2019
Gautam Krishna, Yan Han, Co Tran, Mason Carnahan, Ahmed H Tewfik

In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model and Long short-term Memory (LSTM) regression based model. Our results demonstrate that EEG features can be used to clean speech recorded in presence of background noise.

* To be submitted to ICASSP 2020. arXiv admin note: text overlap with arXiv:1906.08044, arXiv:1906.08871, arXiv:1906.08045, arXiv:1908.05743 

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