On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR

Lam, Tsz Kin; Ohta, Mayumi; Schamoni, Shigehiko; Riezler, Stefan

doi:10.21437/interspeech.2021-1679

Cited by 15 publications

(10 citation statements)

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“…Third, AlexNet and VGGNet architecture have some inspirable benefits compared to other CNN architectures such as easy GoogleNet, ResNet, DenseNet, and especially less parameters are required to train the models and result is lightweight of model. Fourth, because of using some diverse mechanisms such as early stopping [ 59 ] that helps to stop over-fitting of the models, weighted random sampler [ 58 ] for reducing the class imbalance problem of the samples, Adam optimizer [ 38 ] for handling the minimum validation loss and quick training, on-the-fly augmentation [ 61 ], stratified evaluation strategy for ensuring the samples from each class to each fold, and reducing the effect of the class imbalance problem [ 62 , 63 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most data in medicals and clinics are normal and only a few numbers of data are abnormal. Some anterior arrhythmia works performed augmentation manually but herein we have performed online augmentation on-the-fly [ 61 ] of images. The major benefits of this concept are hassle-free and time-saving unlike manual augmentation.…”

Section: Methodsmentioning

confidence: 99%

“… A state-of-the-art improvement in performance is acquired for both proposed models with the 2D transformed images as the input of deep models in ECG arrhythmia classification, which expresses high model generalization. The achievement of model performance is due to the usage of several diverse regularization techniques (BN [ 37 ] and dropout [ 36 ]), Adam optimizer [ 38 ], weighted random sampler [ 58 ], early stopping [ 59 ], on-the-fly data augmentation [ 61 ], and proper initialization of layers [ 62 , 63 ] in the designed models. As such, data imbalance shortcoming on the publicly available datasets, even on clinical or own producing data, could be overcome.…”

Section: Introductionmentioning

confidence: 99%

“…The achievement of model performance is due to the usage of several diverse regularization techniques (BN [ 37 ] and dropout [ 36 ]), Adam optimizer [ 38 ], weighted random sampler [ 58 ], early stopping [ 59 ], on-the-fly data augmentation [ 61 ], and proper initialization of layers [ 62 , 63 ] in the designed models. As such, data imbalance shortcoming on the publicly available datasets, even on clinical or own producing data, could be overcome.…”

Section: Introductionmentioning

confidence: 99%

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An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

Ullah

Heyat

AlSalman

et al. 2022

Journal of Healthcare Engineering

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Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

Ullah

Heyat

AlSalman

et al. 2022

Journal of Healthcare Engineering

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“… The proposed model expresses model generalization because it was tested on four datasets without changing any hyperparameters, and the model architecture and results are consistent. This achievement is due to the use of some diverse regularization strategies: batch normalization (BN) [ 45 ], call-back features [ 46 ], weighted random sampler [ 47 ], Adam optimizer [ 48 ], on-the-fly augmentation [ 49 ], and appropriate initialization of layers [ 50 ] of the model in the method. …”

Section: Introductionmentioning

confidence: 99%

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Ullah

Heyat

Akhtar

et al. 2022

Computational Intelligence and Neuroscience

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Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.

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Dialect Adaptation and Data Augmentation for Low-Resource ASR: Taltech Systems for the Madasr 2023 Challenge

Alumäe,

Kong,

Robnikov

2023

2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

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On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR

Cited by 15 publications

References 0 publications

An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Dialect Adaptation and Data Augmentation for Low-Resource ASR: Taltech Systems for the Madasr 2023 Challenge

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