Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

Art�culos similares

A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions

Acceso

Xiaoping Huang, Yujian Zhou and Yajun Du

In recent years, there has been rapid development in machine learning for solving artificial intelligence tasks in various fields, including translation, speech, and image processing. These AI tasks are often interconnected rather than independent. One s... ver m�s

Revista: Applied Sciences

Es-Tacotron2: Multi-Task Tacotron 2 with Pre-Trained Estimated Network for Reducing the Over-Smoothness Problem

Acceso

Yifan Liu and Jin Zheng

Text-to-speech synthesis is a computational technique for producing synthetic, human-like speech by a computer. In recent years, speech synthesis techniques have developed, and have been employed in many applications, such as automatic translation applic... ver m�s

Revista: Information

Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

Acceso

Jeih-weih Hung, Jung-Shan Lin and Po-Jen Wu

In recent decades, researchers have been focused on developing noise-robust methods in order to compensate for noise effects in automatic speech recognition (ASR) systems and enhance their performance. In this paper, we propose a feature-based noise-robu... ver m�s

Revista: Applied System Innovation

Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

Art�culos similares

Revistas destacadas