Voice activity detection using discriminative restricted Boltzmann machines

Borin, Rogerio G.; Silva, Magno T. M.

doi:10.23919/eusipco.2017.8081262

Cited by 3 publications

(1 citation statement)

References 21 publications

(28 reference statements)

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“…This is a mathematically complex feature set motivated from the source-filter model (see Fig. 5) which is also used for VADs [52], [53]. The usage limitation of features on the FEx architecture will be discussed in Section III-C.…”

Section: E Featuresmentioning

confidence: 99%

Hardware Implementations for Voice Activity Detection: Trends, Challenges and Outlook

Yadav

Legaspi

Alink

et al. 2023

IEEE Trans. Circuits Syst. I

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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-networkbased 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.

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Section: E Featuresmentioning

confidence: 99%