Deep bottleneck features for i-vector based text-independent speaker verification
Sina Hamidi Ghalehjegh, Richard Cameron Rose · 2015
This paper describes the application of deep neural networks (DNNs), trained to discriminate among speakers, to improving performance in text-independent speaker verification. Activations from the bottleneck layer of these DNNs are used as features in an i-vector based speaker verification system. The features derived from this network are thought to be more robust with respect to phonetic variability, which is generally considered to have a negative impact on speaker verification performance. The verification performance using these features is evaluated on the 2012 NIST SRE core-core condition with models trained from a subset of the Fisher and Switchboard conversational speech corpora. It is found that improved performance, as measured by the minimum detection cost function (minDCF), can be obtained by appending speaker discriminative features to the more widely used mel-frequency cepstrum coefficients.