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 …

Rainfall forecasting is crucial in the field of meteorology and hydrology. However, existing
solutions always achieve low prediction accuracy for short-term rainfall forecasting …

Deep neural networks (DNNs) have gained remarkable success in speech recognition,
partially attributed to the flexibility of DNN models in learning complex patterns of speech …

This paper presents a study on multilingual deep neural network (DNN) based acoustic
modeling and its application to new languages. We investigate the effect of phone merging …

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 …

近年来, 随着医学影像技术的快速发展, 医学图像分析步入大数据时代, 如何从海量的医学图像
数据中挖掘出有用信息, 对医学图像识别带来巨大的挑战. 深度学习是机器学习的一个新领域 …

This paper introduces deep neural network (DNN)–hidden Markov model (HMM)-based
methods to tackle speech recognition in heterogeneous groups of speakers including …

This study addresses a challenging problem of predicting mean annual precipitation across
arid and semi-arid areas in northern Algeria, utilizing deterministic, geostatistical (GS), and …

In this work, we explore a data scheduling strategy for semi-supervised learning (SSL) for
acoustic modeling in automatic speech recognition. The conventional approach uses a seed …

The great success of deep learning shows that its technology contains profound truth, and
understanding its internal mechanism not only has important implications for the …