The IBM 2016 Speaker Recognition System
Table 3: Performance comparison of IBM speaker recognition systems with fMLLR features for 2k, 4k, and 10k DNN senones on extended core condition 5 in the NIST SRE 2010.
['System', '#Senones', 'EER [%]', 'minDCF08', 'minDCF10']
In the next set of experiments, we investigated the impact of the number of senones on speaker recognition performance. “tel-tel” condition which are obtained with i-vectors computed using 2k, 4k, and 10k DNN senones and fMLLR features. Two important observations can be made from this table. First, the larger the number of senones, the better the performance. This is due to the discriminative nature of the DNN where increasing the granularity in the output layer improves the model ability in distinguishing among the various phonetic events. Second, irrespective of the number of senones used to calculate the sufficient statistics, the NDA based systems consistently perform better than the LDA based systems. We note that, in our experiments, increasing the number of senones beyond 10k did not yield much gains in performance.