Discriminative Training of Gender-Dependent Acoustic Models

The main goal of this paper is to explore the methods of genderdependent acoustic modeling that would take the possibly of imperfect function of a gender detector into consideration. Such methods will be beneficial in realtime recognition tasks (eg. real-time subtitling of meetings) when the automatic gender detection is delayed or incorrect. The goal is to minimize an impact to the correct function of the recognizer. The paper also describes a technique of unsupervised splitting of training data, which can improve gender-dependent acoustic models trained on the basis of manual markers (male/female). The idea of this approach is grounded on the fact that a significant amount of ”masculine” female and ”feminine” male voices occurring in training corpora and also on frequent errors in manual markers.