Utterance verification of short keywords using hybrid neural-network/HMM approach

We focus on utterance verification of short keywords, which contain only one syllable. The relative less information makes the task rather tough. We integrate neural network and hidden Markov models (HMMs) in an attempt to utilize the strength of both. A classifier was built for each keyword. Our model differs from other hybrid models in that we feed the neural networks with posterior likelihood generated by a set of HMMs. Therefore, statistical information can be reserved and the size of neural networks is not too large. Another feature is our utilization of word duration. Our experiments were carried out in Mandarin. Two baseline models for comparison were built, one employed the likelihood ratio measure and the other utilized a neural network presented in preceding studies. Two models depending on our approaches were constructed. The results showed that one proposed approach improved by 8.7% and 12.5%, and another by 15.2% and 19.2%