In this paper, we present a new NN/HMM speech recognition system with a NN-base acoustic model and RNN-based language model. The employed neural-network-based acoustic model computes posteriors for states of context-dependent acoustic units. A recurrent neural network with the maximum entropy extension was used as a language model. This hybrid NN/HMM system was compared with our previous hybrid NN/HMM system equipped with a standard n-gram language model. In our experiments, we also compared it to a standard GMM/HMM system. The system performance was evaluated on the British English speech corpus and compared with some previous work.
CITATION STYLE
Soutner, D., Zelinka, J., & Müller, L. (2014). On a hybrid NN/HMM speech recognition system with a RNN-based language model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8773, pp. 315–321). Springer Verlag. https://doi.org/10.1007/978-3-319-11581-8_39
Mendeley helps you to discover research relevant for your work.