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 …

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 …

As a new way of training generative models, Generative Adversarial Net (GAN) that uses a
discriminative model to guide the training of the generative model has enjoyed considerable …

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 …

A field that has directly benefited from the recent advances in deep learning is automatic
speech recognition (ASR). Despite the great achievements of the past decades, however, a …

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

We describe the latest version of Microsoft's conversational speech recognition system for
the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to …

In this paper we describe a method to perform sequencediscriminative training of neural
network acoustic models without the need for frame-level cross-entropy pre-training. We use …

We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech
recognition. Several modeling choices are discussed in this work, including various …

We present a state-of-the-art speech recognition system developed using end-to-end deep
learning. Our architecture is significantly simpler than traditional speech systems, which rely …