Journal of Applied Sciences ›› 2014, Vol. 32 ›› Issue (5): 515-522.doi: 10.3969/j.issn.0255-8297.2014.05.013

• Signal and Information Processing • Previous Articles     Next Articles

Hierarchical Structure of Deep Belief Network for Phoneme Recognition

WANG Yi1,2, YANG Jun-an1,2, LIU Hui1,2, LIU Lin3, LU Gao4   

  1. 1. Room 404, Electronic Engineering Institute, Hefei 230037, China
    2. Key Laboratory of Electronic Restriction, Anhui Province, Hefei 230037, China
    3. Anhui USTC iFlytek Corporation, Hefei 230037, China
    4. No.52 Sub Unit, No.77108 Unit, Chengdu 611233, China
  • Received:2013-09-08 Revised:2014-03-28 Online:2014-09-23 Published:2014-03-28

Abstract: To overcome the problem of poor recognition performance and being prone to be trapped in local
optima, this paper proposes a hierarchical phoneme classification method based on deep belief network (DBN).
The system consists of two parts: a bottleneck feature and a phoneme classifier, both DBN based. The two
parts are combined to form a phoneme recognition system. The system can extract low dimensional and
supervising features, and improve classification accuracy. Experiments on TIMIT corpus suggest that the
proposed system can obtain 18.5% phoneme error rate as compared with existing systems.

Key words: phoneme recognition, hierarchical structure, deep belief network, bottleneck feature

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