New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as
originated by LE Baum and T. Petrie (1966) and gives practical details on methods of …

The environmental robustness of DNN-based acoustic models can be significantly improved
by using multi-condition training data. However, as data collection is a costly proposition …

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

We investigate the effectiveness of generative adversarial networks (GANs) for speech
enhancement, in the context of improving noise robustness of automatic speech recognition …

This work presents a large-scale audio-visual speech recognition system based on a
recurrent neural network transducer (RNN-T) architecture. To support the development of …

The use of hidden Markov models for speech recognition has become predominant in the
last several years, as evidenced by the number of published papers and talks at major …

Major progress is being recorded regularly on both the technology and exploitation of
automatic speech recognition (ASR) and spoken language systems. However, there are still …

We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the
device to preserve only the speech signals from a target user, as part of a streaming speech …

Abstract Systems and methods for speech recognition incorporating environmental variables
are provided. The systems and methods capture speech to be recognized. The speech is …