Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Despite the rapid progress of automatic speech recognition (ASR) technologies in the past
few decades, recognition of disordered speech remains a highly challenging task to date …

Recent work on automated augmentation strategies has led to state-of-the-art results in
image classification and object detection. An obstacle to a large-scale adoption of these …

We present SpecAugment, a simple data augmentation method for speech recognition.
SpecAugment is applied directly to the feature inputs of a neural network (ie, filter bank …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

Data augmentation is a common strategy adopted to increase the quantity of training data,
avoid overfitting and improve robustness of the models. In this paper, we investigate audio …

Speech enhancement and automatic speech recognition (ASR) are most often evaluated in
matched (or multi-condition) settings where the acoustic conditions of the training data …

This paper investigates data augmentation for deep neural network acoustic modeling
based on label-preserving transformations to deal with data sparsity. Two data …

Contrastive Predictive Coding (CPC), based on predicting future segments of speech from
past segments is emerging as a powerful algorithm for representation learning of speech …