Multimodal Speaker Verification Based on Electroglottograph Signal and Glottal Activity Detection

To achieve robust speaker verification, we propose a multimodal method which includes additional nonaudio features and glottal activity detector. As a nonaudio sensor an electroglottograph (EGG) is applied. Parameters of EGG signal are used to augment conventional audio feature vector. Algorithm for EGG parameterization is based on the shape of the idealized waveform and glottal activity detector. We compare our algorithm with conventional one in the term of verification accuracy in high noise environment. All experiments are performed using Gaussian Mixture Model recognition system. Obtained results show a significant improvement of the text-independent speaker verification in high noise environment and opportunity for further improvements in this area.