[PDF][PDF] Recent advances in end-to-end automatic speech recognition
J Li - APSIPA Transactions on Signal and Information …, 2022 - nowpublishers.com
Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …
A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability
Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …
networks. The basic idea of DA is to construct new training data to improve the model's …
STEMM: Self-learning with speech-text manifold mixup for speech translation
How to learn a better speech representation for end-to-end speech-to-text translation (ST)
with limited labeled data? Existing techniques often attempt to transfer powerful machine …
with limited labeled data? Existing techniques often attempt to transfer powerful machine …
[HTML][HTML] A new approach for detecting fundus lesions using image processing and deep neural network architecture based on YOLO model
Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it
presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and …
presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and …
An End‐to‐End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
Electrocardiography (ECG) is a well‐known noninvasive technique in medical science that
provides information about the heart's rhythm and current conditions. Automatic ECG …
provides information about the heart's rhythm and current conditions. Automatic ECG …
[HTML][HTML] An automatic premature ventricular contraction recognition system based on imbalanced dataset and pre-trained residual network using transfer learning on …
The development of automatic monitoring and diagnosis systems for cardiac patients over
the internet has been facilitated by recent advancements in wearable sensor devices from …
the internet has been facilitated by recent advancements in wearable sensor devices from …
[Retracted] An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization …
Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning
approaches is becoming popular in clinical diagnosis systems due to its good prognosis …
approaches is becoming popular in clinical diagnosis systems due to its good prognosis …
Sample, translate, recombine: Leveraging audio alignments for data augmentation in end-to-end speech translation
End-to-end speech translation relies on data that pair source-language speech inputs with
corresponding translations into a target language. Such data are notoriously scarce, making …
corresponding translations into a target language. Such data are notoriously scarce, making …
Optimizing data usage for low-resource speech recognition
Automatic speech recognition has made huge progress recently. However, the current
modeling strategy still suffers a large performance degradation when facing the low …
modeling strategy still suffers a large performance degradation when facing the low …
Fast random approximation of multi-channel room impulse response
The training of modern neural-network-based speech processing systems typically requires
a large amount of reverberant data to make the systems robust against reverberation …
a large amount of reverberant data to make the systems robust against reverberation …