Abstract
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.
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Soutner, D., Zelinka, J., Müller, L. (2014). On a Hybrid NN/HMM Speech Recognition System with a RNN-Based Language Model. In: Ronzhin, A., Potapova, R., Delic, V. (eds) Speech and Computer. SPECOM 2014. Lecture Notes in Computer Science(), vol 8773. Springer, Cham. https://doi.org/10.1007/978-3-319-11581-8_39
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DOI: https://doi.org/10.1007/978-3-319-11581-8_39
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