Hidden neural networks: application to speech recognition

In this paper we evaluate the Hidden Neural Network HMM/NN hybrid presented at last years ICASSP on two speech recognition benchmark tasks; 1) task independent isolated word recognition on the PHONEBOOK database, and 2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how Hidden Neural Networks (HNNs) with much fewer parameters than conventional HMMs and other hybrids can obtain comparable performance, and for the broad class task it is illustrated how the HNN can be applied as a purely transition based system, where acoustic context dependent transition probabilities are estimated by neural networks.