On a Hybrid NN/HMM Speech Recognition System with a RNN-Based Language Model
| Autor: | Daniel Soutner, Jan Zelinka, Luděk Müller |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
Recurrent neural network Artificial neural network Computer Science::Sound Computer science Principle of maximum entropy Speech recognition Acoustic model Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Language modelling Speech corpus Language model Hidden Markov model |
| Zdroj: | Speech and Computer ISBN: 9783319115801 SPECOM |
| DOI: | 10.1007/978-3-319-11581-8_39 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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