A Case Study on Back-End Voice Activity Detection for Distributed Specch Recognition System Using Support Vector Machines
Azzedine Touazi, Mohamed Debyeche · 2014
Recently, the Voice Activity Detection (VAD) algorithms based on machine learning techniques have shown impressive results in the area of speech recognition. In this paper, we present a case study and we discuss the performance of VAD based on Support Vector Machines (SVM) for Distributed Speech Recognition (DSR) system. In this case study, the speech and the non-speech frames are detected from the compressed Mel Frequency Cepstral Coefficients (MFCCs), at the back-end (e.g. Server) side, with the aim of improving the VAD performance and reducing the compression bit-rate from the front-end side. By using the trained SVM with polynomial kernel, the SVM-based VAD can produce encouraging detection results. The classification task conducted from the Aurora-2 speech database with different noise conditions shows comparable VAD performance, with respect to ETSI Advanced Front-End (ETSI-AFE) standard.