As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

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 …

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art
conventional models with respect to both quality, ie, word error rate (WER), and latency, ie …

Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been
shown to significantly improve speech recognition performance over the conventional …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

Speech emotion recognition is a challenging problem partly because it is unclear what
features are effective for the task. In this paper we propose to utilize deep neural networks …

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 …

Monaural source separation is important for many real world applications. It is challenging
because, with only a single channel of information available, without any constraints, an …

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 …