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 …

In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

The performance of automatic speech recognition (ASR) has improved tremendously due to
the application of deep neural networks (DNNs). Despite this progress, building a new ASR …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

Deep learning is becoming a mainstream technology for speech recognition at industrial
scale. In this paper, we provide an overview of the work by Microsoft speech researchers …

We propose to adapt deep neural network (DNN) acoustic models to a target speaker by
supplying speaker identity vectors (i-vectors) as input features to the network in parallel with …

We propose a novel regularized adaptation technique for context dependent deep neural
network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output …

Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

This paper proposes a simple yet effective model-based neural network speaker adaptation
technique that learns speaker-specific hidden unit contributions given adaptation data …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …