TY - JOUR
T1 - Hardware implementations for voice activity detection: trends, challenges and outlook
AU - Yadav, Shubham
AU - Legaspi, Patrice Abbie David
AU - Oude Alink, Mark Stefan
AU - Kokkeler, Andre B.J.
AU - Nauta, Bram
N1 - Funding Information:
This work was supported in part by the Project Analog Approximate Accelerators (AAA) under Project 17703 and in part by the Research open technology programme (OTP) through the Dutch Research Council (NWO).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Voice Activity Detection (VAD) is a technique used to identify the presence of human voice in an audio signal. It is implemented as an always-on component in most speech processing applications. As speech is absent most of the time, this component typically dominates the overall average power consumption of the system (excluding microphone). The widespread usage in speech applications and the need for ultra low power VAD have led to a plethora of algorithms and implementations in the hardware domain, necessitating a comprehensive study and analysis to understand (real-time) requirements, different design parameters, testing strategies, but also to identify design trends, challenges and guidelines for future implementations and testing of VAD devices. A scoping review was conducted to identify the articles for hardware implementations of VAD from January 2010 -December 2021, the results of which are presented in this article. The results highlight a big design space being used for VAD along with a lack of standard testing methodology and usage of application-dependent performance metrics. An increased usage of filter-based feature extractors along with neural-network-based classifiers is observed. Due to lack of standardisation, no other trends can be established from the results. A set of rules and guidelines are therefore provided to facilitate the future development and benchmarking of VADs.
AB - Voice Activity Detection (VAD) is a technique used to identify the presence of human voice in an audio signal. It is implemented as an always-on component in most speech processing applications. As speech is absent most of the time, this component typically dominates the overall average power consumption of the system (excluding microphone). The widespread usage in speech applications and the need for ultra low power VAD have led to a plethora of algorithms and implementations in the hardware domain, necessitating a comprehensive study and analysis to understand (real-time) requirements, different design parameters, testing strategies, but also to identify design trends, challenges and guidelines for future implementations and testing of VAD devices. A scoping review was conducted to identify the articles for hardware implementations of VAD from January 2010 -December 2021, the results of which are presented in this article. The results highlight a big design space being used for VAD along with a lack of standard testing methodology and usage of application-dependent performance metrics. An increased usage of filter-based feature extractors along with neural-network-based classifiers is observed. Due to lack of standardisation, no other trends can be established from the results. A set of rules and guidelines are therefore provided to facilitate the future development and benchmarking of VADs.
KW - Voice
KW - Speech
KW - Voice activity detection
KW - Voice detection
KW - Review
U2 - 10.1109/TCSI.2022.3225717
DO - 10.1109/TCSI.2022.3225717
M3 - Article
SN - 1549-8328
VL - 70
SP - 1083
EP - 1096
JO - IEEE transactions on circuits and systems I: regular papers
JF - IEEE transactions on circuits and systems I: regular papers
IS - 3
ER -