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- W2895584523 abstract "In view of the unreasonable conditional hypotheses of hidden Markov model in speech recognition, an acoustic model based on bidirectional long short-term network was proposed after further studying the ability of recurrent neural network. We have successfully applied the training criterion based on connectionist temporal classification to the training of this acoustic model, and built our end-to-end Chinese speech recognition system without hidden Markov model. Meanwhile, a speech decoding method base on weighted finite-state transducer was designed to effectively solve the problem that lexicon and language model are difficult to integrate into the decoding process. Compared with the traditional GMM-HMM system and the hybrid DNN-HMM system, the experimental results show our end-to-end system significantly reduces the recognition error rate, while at the same time speeding up decoding dramatically. It is shown that the acoustic model proposed in this paper can effectively enhance the model discrimination and optimize the structure of speech recognition system." @default.
- W2895584523 created "2018-10-12" @default.
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- W2895584523 date "2018-10-05" @default.
- W2895584523 modified "2023-09-27" @default.
- W2895584523 title "End-to-End Mandarin Speech Recognition Using Bidirectional Long Short-Term Memory Network" @default.
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- W2895584523 doi "https://doi.org/10.1007/978-3-030-00214-5_91" @default.
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