Voice activity detection based on conditional random fields using multiple features
Akira Saito, Yoshihiko Nankaku, Akinobu Lee, Keiichi Tokuda · 2010
This paper proposes a Voice Activity Detection (VAD) algorithm based on Conditional Random Fields (CRF) using multiple features.VAD is a technique used to distinguish between speech and non-speech in noisy environments and is an important component in many real-world speech applications.The posterior probability of output labels in the proposed method is directly modeled by the weighted sum of the feature functions.Effective features are automatically selected by estimating appropriate weight parameters to improve the accuracy of VAD.Experimental results on the CENSREC-1-C database revealed that the proposed approach can decrease error rates by using CRF.