Gender aware Deep Boltzmann Machines for phone recognition

Recently Deep neural networks (DNN) have achieved a lot of success and become the most popular approach for speech recognition. DNN training for speech recognition is a difficult process due to its large number of parameters and speech dataset size. Using DNNs in a modeling task can be improved when pre-training is done using additional information. In this paper, we propose a new approach namely Gender-aware Deep Boltzmann Machine (GADBM) for pre-training of DNNs which utilizes gender information for better recognition task. The proposed pre-training method is evaluated in a phone recognition task. Experimental results on TIMIT dataset shows that the proposed method outperforms Deep Belief Network and basic Deep Boltzmann Machine.