Fusing high- and low-level features for speaker recognition

Abstract

The area of automatic speaker recognition has been dominated by systems using only short-term, low-level acoustic information, such as cepstral features. While these systems have produced low error rates, they ignore higher levels of information beyond low-level acoustics that convey speaker information. Recently published works have demonstrated that such high-level information can be used successfully in automatic speaker recognition systems by improving accuracy and potentially increasing robustness. Wide ranging high-levelfeature-based approaches using pronunciation models, prosodic dynamics, pitch gestures, phone streams, and conversational interactions were explored and developed under the SuperSID