TY  - JOUR
A1  - Triantafyllopoulos, Andreas
A1  - Schuller, Bjorn W.
A1  - Iymen, Gokce
A1  - Sezgin, Metin
A1  - He, Xiangheng
A1  - Yang, Zijiang
A1  - Tzirakis, Panagiotis
A1  - Liu, Shuo
A1  - Mertes, Silvan
A1  - André, Elisabeth
A1  - Fu, Ruibo
A1  - Tao, Jianhua
T1  - An overview of affective speech synthesis and conversion in the deep learning era
T2  - Proceedings of the IEEE
N2  - Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human–computer interaction research. In recent years, machines have managed to master the art of generating speech that is understandable by humans. However, the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions—aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesized utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology that underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In this overview, we outline ongoing trends and summarize state-of-the-art approaches in an attempt to provide a broad overview of this exciting field.
KW  - Electrical and Electronic Engineering
Y1  - 2023
UR  - https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/104403
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1044036
DO  - 10.1109/jproc.2023.3250266
SN  - 0018-9219
SN  - 1558-2256
VL  - 111
IS  - 10
SP  - 1355
EP  - 1381
PB  - IEEE
CY  - Piscataway, NJ
ER  -