This book provides a comprehensive and systematic treatment of Foreign Accent Conversion (FAC), an emerging field at the intersection of speech synthesis, deep learning, and speech signal processing. It begins with an accessible introduction to the foundational concepts of accent and its acoustic characteristics, including temporal, spectral, prosodic, and integrated speech features. This book then presents a structured survey of FAC methods, progressing from traditional approaches—such as voice morphing, articulatory synthesis, and frame-pairing—to state-of-the-art deep learning techniques, including sequence-to-sequence models, TTS-guided methods, and discrete speech token-based approaches. Each method category is explained with its core pipeline, advantages, limitations, and representative studies. This book also covers commonly used datasets and evaluation metrics (both subjective and objective), critically examines the current limitations and challenges facing the field, and outlines promising future research directions. A dedicated chapter provides hands-on resources—including publicly available datasets and code repositories—to help newcomers get started with FAC research. Written for both beginners and experienced researchers, this book serves as an essential reference for anyone interested in understanding, developing, or applying accent conversion technology.

Chenxi Liu is a Ph.D. student in the Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering at the Technion - Israel Institute of Technology, Haifa, Israel, under the supervision of Prof. Israel Cohen. Her research focuses on foreign accent conversion and deep learning. She received her M.Sc. degree in Electrical and Computer Engineering from the Technion in 2023, with a thesis on machine-learning-based signal processing and classification of nanomaterial-based sensor arrays. She received her B.Sc. degree in Software Engineering (Digital Media Technology) from Dalian University of Technology, China, in 2020. She has published in the Journal of Low Power Electronics and Applications, has a feature article under review at IEEE Signal Processing Magazine, and has a conference paper under review at the 19th International Workshop on Acoustic Signal Enhancement (IWAENC). She was a meritorious winner of the MCM/ICM Mathematical Contest in Modeling (2019).

Israel Cohen is the Louis and Samuel Seiden Professor of Electrical and Computer Engineering at the Technion - Israel Institute of Technology, Haifa, Israel. He is an IEEE Fellow “for contributions to the theory and application of speech enhancement” (since 2015) and was a distinguished lecturer of the IEEE Signal Processing Society (2019–2020). He received his B.Sc. (Summa Cum Laude), M.Sc., and Ph.D. degrees in Electrical Engineering from the Technion in 1990, 1993, and 1998, respectively. He was a Research Scientist with RAFAEL Research Laboratories, Israel Ministry of Defense (1990–1998), and a Postdoctoral Research Associate at the Computer Science Department, Yale University (1998–2001). He is a coauthor of Fundamentals of Signal Enhancement and Array Signal Processing (Wiley-IEEE Press, 2018), Array Beamforming with Linear Difference Equations (Springer, 2021), Array Processing - Kronecker Product Beamforming (Springer, 2019), and several other books published by Springer.