Multiclass Discriminative Training of i-vector Language Recognition
Alan V. McCree · 2014
The current state-of-the-art for acoustic language recognition is an i-vector classifier followed by a discriminatively-trained multiclass back-end. This paper presents a unified approach, where a Gaussian i-vector classifier is trained using Maximum Mutual Information (MMI) to directly optimize the multiclass calibration cri-terion, so that no separate back-end is needed. The sys-tem is extended to the open set task by training an ad-ditional Gaussian model. Results on the NIST LRE11 standard evaluation task confirm that high performance is maintained with this new single-stage approach. 1.