NONLINEAR DISCRIMINANT FEATURE EXTRACTION FOR ROBUST TEXT-INDEPENDENT SPEAKER RECOGNITION
Yochai Konig, Larry P. Heck, Mitchel Weintraub, Kemal Sönmez · 1997
We study a nonlinear discriminant analysis (NLDA) technique that extracts a speaker-discriminant feature set. Our approach is to train a multilayer perceptron (MLP) to maximize the separation between speakers by nonlinearly projecting a large set of acoustic features (e.g., several frames) to a lower-dimensional feature set. The extracted features are optimized to discriminate between speakers and to be robust to mismatched training and testing conditions. We train the MLP on a development set and apply it to the training and testing utterances. Our results show that by combining the NLDA-based system with a state of the art cepstrum-based system we improve the speaker verification performance on the 1997 NIST Speaker Recognition Evaluation set by 15% in average compared with our cepstrum-only system. 1. INTRODUCTION Our goal is to extract and select features that are more invariant to non-speaker-related conditions such as handset type, sentence content, and channel effects. Such fe...