One of the most amazing functions of the human auditory system is the ability to detect all
kinds of sound events in the environment. With the technologies and hardware advances …

Methods, including computer programs encoded on a com puter storage medium, for
enhancing the processing of audio waveforms for speech recognition using various neural …

This paper investigates a multi-channel denoising autoencoder (DAE)-based speech
enhancement approach. In recent years, deep neural network (DNN)-based monaural …

In this paper, we propose two approaches to improve deep neural network (DNN) acoustic
models for speech recognition in reverberant environments. Both methods utilize auxiliary …

This work deals with non-native children's speech and investigates both multi-task and
transfer learning approaches to adapt a multi-language Deep Neural Network (DNN) to …

Convolutional Neural Networks (CNNs) are superior to fully connected neural networks in
various speech recognition tasks and the advantage is pronounced in noisy environments …

This paper examines the individual and combined impacts of various front-end approaches
on the performance of deep neural network (DNN) based speech recognition systems in …

A challenge for speech recognition for voice-controlled household devices, like the Amazon
Echo or Google Home, is robustness against interfering background speech. Formulated as …

This paper addresses a front-end system for speech recognition of spontaneous
conversational speech signals that are recorded with asynchronous distributed microphones …

Recent end-to-end models for automatic speech recognition use sensory attention to
integrate multiple input channels within a single neural network. However, these attention …