LEAP System for SRE19 CTS Challenge - Improvements and Error Analysis
Table 2: Impact of adding in-domain and out-of-domain data in the training of backend models. The front-end x-vectors for these models come from the XV1 system.
['Model', 'Train Datasets', 'SRE18 Dev EER (%)', 'SRE18 Dev  [ITALIC] CMin', 'SRE18 Eval EER (%)', 'SRE18 Eval  [ITALIC] CMin', 'SRE19 Eval EER (%)', 'SRE19 Eval  [ITALIC] CMin']
Using the same front-end embedding extractor (XV1 configuration), we compare the two backend approaches based on generative GPLDA model as well as the neural PLDA model. We train GPLDA models with Kaldi using SRE 04-10, switchboard corpus and Mixer6. To this, we add SRE16 and SRE18 data to study the improvements of adding more data. We then initialize the NPLDA model using the GPLDA back-end parameters and retrain the model using the cost function proposed. In all the cases, the NPLDA yields significant improvements over PLDA. The improvements are more significant for the SRE cost function (CMin) as the model is optimized for that metric.