Fast adaptation of deep neural networks (DNN) is an important research topic in deep
learning. In this paper, we have proposed a general adaptation scheme for DNN based on …

This paper presents an investigation of far field speech recognition using beamforming and
channel concatenation in the context of Deep Neural Network (DNN) based feature …

In the hybrid approach, neural network output directly serves as hidden Markov model
(HMM) state posterior probability estimates. In contrast to this, in the tandem approach …

The human ability to classify acoustic sounds is still unmatched compared to recent methods
in machine learning. Psychoacoustic and physiological studies indicate that the auditory …

Recently several speaker adaptation methods have been proposed for deep neural network
(DNN) in many large vocabulary continuous speech recognition (LVCSR) tasks. However …

This paper investigates the application of hierarchical MRASTA bottleneck (BN) features for
under-resourced languages within the IARPA Babel project. Through multilingual training of …

In the tandem approach, the output of a neural network (NN) serves as input features to a
Gaussian mixture model (GMM) aiming to improve the emission probability estimates. As …

Abstract Recently, a multilingual Multi Layer Perceptron (MLP) training method was
introduced without having to explicitly map the phonetic units of multiple languages to a …

In this paper we describe RWTH Aachen's system for keyword search (KWS) with very
limited amount of transcribed audio data available in the target language. This setting has …

Features inspired by the auditory system have previously demonstrated improvement in
automatic speech recognition (ASR). Similarly, the use of Deep Neural Networks (DNN) was …