Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …

Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep
learning techniques have drawn ever-increasing research interests because of their …

In this paper, we propose a unified pre-training approach called UniSpeech to learn speech
representations with both labeled and unlabeled data, in which supervised phonetic CTC …

Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic
speech recognition (ASR) coverage of the world's languages. They have shown …

Training a conventional automatic speech recognition (ASR) system to support multiple
languages is challenging because the sub-word unit, lexicon and word inventories are …

Speech self-supervised learning has attracted much attention due to its promising
performance in multiple downstream tasks, and has become a new growth engine for …

This article introduces the deep neural network method into the field of high-dimensional
microwave modeling. Deep learning is nowadays highly successful in solving complex and …

Recent advancements in deep learning, particularly convolutional networks, have rapidly
become the preferred methodology for analyzing medical images, facilitating tasks like …

Highlights•A variety of different machine learning methods have been used in cognitive
science.•Despite their differences, these systems can each provide human-like …

Autonomous driving requires reliable and accurate detection and recognition of surrounding
objects in real drivable environments. Although different object detection algorithms have …