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

Speech emotion recognition could be considered a new topic in speech processing where
he plays that plays an essential role in human interaction. Emotions are a king of speech …

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 …

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 …

We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes
speech utterances directly to characters without pronunciation models, HMMs or other …

Abstract Models comprised of deep convolutional network layers have dominated recent
image interpretation tasks; we investigate whether models which are also compositional, or" …

We investigate training end-to-end speech recognition models with the recurrent neural
network transducer (RNN-T): a streaming, all-neural, sequence-to-sequence architecture …

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-
purpose NLP tool that has proven effective for many text-generation and sequence-labeling …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

Real-life recommender systems often face the daunting task of providing recommendations
based only on the clicks of a user session. Methods that rely on user profiles--such as matrix …