Intelligent Speech Processing Technology

Erscheinungsjahr 2026
ISBN: 978-3-032-35655-0
Verlag: Springer

Buch, Englisch, 129 Seiten, Format (B × H): 155 mm x 235 mm

This book provides a comprehensive overview of the latest research breakthroughs and technological applications in modern speech technology, with a special focus on deep learning and multimodal integration. The book explores how advances in machine learning, attention mechanisms, and cross-modal information fusion are reshaping the field. It addresses current limitations in noise robustness, emotion perception, contextual understanding, and human-like speech synthesis, providing innovative solutions through both theoretical frameworks and cutting-edge models. The aim is to offer a unified view of how speech systems can be enhanced in accuracy, naturalness, and interactivity. By integrating perspectives from linguistics, signal processing, and affective computing, the book appeals to an interdisciplinary audience seeking to understand and shape the future of speech-based intelligence.

Zielgruppe

Research

Autoren/Hrsg.

Wang, Liejun

Yu, Yinfeng

Tao, Xiaoming

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik

Inhaltsverzeichnis

Introduction.- The Development History of Speech Technology.- Overview of Speech Technology.- Speech Emotion Recognition.- MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative.- Emotion Recognition in Speech via Progressive Channel Querying.- BSS-CFFMA:BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding.- BSP-MPNet: Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boosting.- Speech Recognition.- Speech Synthesis.- Conclusion.

Über Autor(innen)

Liejun Wang is a Professor in the School of Computer Science and Technology at Xinjiang University. His research interests include multimedia information processing, computer vision, and intelligent information systems. He has led more than 50 research projects, including national-level and provincial-level projects, and has published more than 170 academic papers. He has received several awards and honors, including the Medal for Development and Construction of Xinjiang, the Baosteel Excellent Teacher Award, the Tianshan Talents High-Level Leading Talent Program, and the Second Prize of the Natural Science Award of Xinjiang Uygur Autonomous Region.

Yinfeng Yu is an Associate Professor at Xinjiang University. He received his PhD degree in Computer Science and Technology from Tsinghua University in 2023. His research interests include artificial intelligence, intelligent robotics, speech interaction, remote sensing image analysis, and multimedia information processing. He has published papers in leading international conferences such as IJCAI and ICLR and has served as a reviewer for major conferences including ICASSP, CVPR, ICCV, and ECCV.

Xiaoming Tao is a Tenured Professor in the Department of Electronic Engineering at Tsinghua University and a recipient of the National Science Fund for Distinguished Young Scholars. Her research interests include wireless multimedia communication, multimedia computational communication, and semantic communication. She has received several major awards, including the China Young Women Scientist Award, the China Youth Science and Technology Award, the Xplorer Prize, and national-level science and technology awards.