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Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing
Author(s) -
Jung Young Hoon,
Hong Seong Kwang,
Wang Hee Seung,
Han Jae Hyun,
Pham Trung Xuan,
Park Hyunsin,
Kim Junyeong,
Kang Sunghun,
Yoo Chang D.,
Lee Keon Jae
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201904020
Subject(s) - biometrics , computer science , piezoelectricity , software , sensitivity (control systems) , interface (matter) , speech recognition , authentication (law) , acoustic sensor , human–computer interaction , acoustics , artificial intelligence , electronic engineering , engineering , electrical engineering , physics , computer security , bubble , maximum bubble pressure method , parallel computing , programming language
Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self‐powered sensors, machine learning algorithms, and speaker recognition.