2023 RoEduNet Conference: Networking in Education and Research

Improved Speech Activity Detection Model Using Convolutional Neural Networks

22 Sept 2023, 11:20

20m

Aula Mihai I (University of Craiova)

Paper presentation Pervasive Systems and Computing Session C

Speaker

Florin Pop (University Politehnica of Bucharest, Romania / National Institute for Research & Development in Informatics – ICI Bucharest, Romania)

Description

n this study, the performance of four speech/voice
activity detection (VAD) models, namely SpeechBrain, Picovoice,
InaSpeechSegmenter and WebRTC, is compared using data
collected from KAIST (Korea Advanced Institute of Science
and Technology). The goal is to develop an improved VAD
model based on the analysis of SpeechBrain’s performance and
provide a detailed perspective on the differences and similarities
between these models. The study focuses on the technologies
and algorithms used by each model and utilizes experimental
methods to evaluate their performance. The experimental results
show that SpeechBrain performs the best, with an average
recall value of 0.97 and a precision value of 0.96. Our research
endeavors have led to the refinement of the existing VAD model,
resulting in even more compelling performance metrics. The
improved model achieves a recall value of 0.98 and precision
value of 0.97, signifying its enhanced capability to accurately
detect speech activity. These findings hold promise for the future
advancement of VAD models and their application in various
speech-processing domains. This research can be used to enhance
future VAD models and develop more advanced speech processing
applications.
Index Terms—Improved Speech Activity Detection Model,
Convolutional Neural Networks (CNN), VAD (Voice Activity
Detection).