@inproceedings{konig1998nonlinear, author = {Konig, Yochai and Heck, Larry and Weintraub, Mitch and Sonmez, Kemal}, title = {Nonlinear Discriminant Feature Extraction for Robust Text-Independent Speaker Recognition}, booktitle = {RLA2C}, year = {1998}, month = {January}, abstract = {We study a deep neural network (deep learning) 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.}, url = {https://www.microsoft.com/en-us/research/publication/nonlinear-discriminant-feature-extraction-for-robust-text-independent-speaker-recognition/}, edition = {RLA2C}, }