Word-Conditioned Phone N-Grams for Speaker Recognition

We extend the state-of-the-art by applying word-conditioning to constrain phone N-gram features used in speaker recognition. Feature-level combination of 52 word unigrams constraining phone N-grams of order 1, 2, and 3 proved to be the best approach. Our system achieves 18% and 27% improvements compared to a non word-conditioned phone N-grams system on SRE05 and SRE06, respectively. Furthermore, the system achieves 18% and 37% improvements compared to the non word-conditioned phone N-grams system when each system is combined with a GMM-based system on SRE05 and SRE06, suggesting that the word-conditioned features are more complementary. On both corpora, this approach achieves a 4.7% EER standalone, and a 3.3% EER in combination with the non word-conditioned phone N-grams and GMM-based systems. Note that the word-conditioning approach utilizes only 43% of SRE05 data.