Incorporating Auditory Feature Uncertainties in Robust Speaker Identification

Conventional speaker recognition systems perform poorly under noisy conditions. Recent research suggests that binary time-frequency (T-F) masks be a promising front-end for robust speaker recognition. In this paper, we propose novel auditory features based on an auditory periphery model, and show that these features capture significant speaker characteristics. Additionally, we estimate uncertainties of the auditory features based on binary T-F masks, and calculate speaker likelihood scores using uncertainty decoding. Our approach achieves substantial performance improvement in a speaker identification task compared with a state-of-the-art robust front-end in a wide range of signal-to-noise conditions.