Continuous Mandarin speech recognition using hierarchical recurrent neural networks

Yuan Fu Liao*, Wen Yuan Chen, Sin-Horng Chen

*Corresponding author for this work

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

An ANN-based continuous Mandarin base-syllable recognition system is proposed. It adopts a hybrid approach to combine an HRNN with a Viterbi search. The HRNN is taken as a frond-end processor and responsible for calculating discrimination scores for all 411 base-syllables. The Viterbi search is then followed to find out the best base-syllable sequence with highest score as the recognized output. Experimental results showed that the proposed system outperforms the conventional HMM method on both the recognition accuracy and the computational complexity. The system can also be further modified to reduce the computational complexity while retaining the recognition accuracy almost be undegraded.

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