  Learned feature representations and sub-phoneme posteriors from Deep Neural
Networks (DNNs) have been used separately to produce significant performance
gains for speaker and language recognition tasks. In this work we show how
these gains are possible using a single DNN for both speaker and language
recognition. The unified DNN approach is shown to yield substantial performance
improvements on the the 2013 Domain Adaptation Challenge speaker recognition
task (55% reduction in EER for the out-of-domain condition) and on the NIST
2011 Language Recognition Evaluation (48% reduction in EER for the 30s test
condition).
