The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Self-supervised approaches for speech representation learning are challenged by three
unique problems:(1) there are multiple sound units in each input utterance,(2) there is no …

We show that an end-to-end deep learning approach can be used to recognize either
English or Mandarin Chinese speech–two vastly different languages. Because it replaces …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most
powerful dynamic classifiers publicly known. The network itself and the related learning …

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end
training methods such as Connectionist Temporal Classification make it possible to train …

Abstract Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN)
architecture that was designed to model temporal sequences and their long-range …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been
shown to significantly improve speech recognition performance over the conventional …