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 articlepeer-review

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.

Original languageEnglish
Article number550600
Pages (from-to)3370-3373
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
DOIs
StatePublished - 9 May 1996
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: 7 May 199610 May 1996

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