An on-line adaptive neural network for speech recognition

Li Peng Zhang, Li Me Li, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


In this paper, we present an on-line learning neural network model, Dynamic Recognition Neural Network (DRNN), for real-time speech recognition. The property of accumulative learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning. A comparison between the DRNN and Hidden Markov Model (HMM) shows that the computational complexity of the former is lower than that of the latter in both training and recognition. Encouraging results are obtained when the DRNN is tested on a BUPT digit database (Mandarin) and on the on-line learning of twenty isolated English computer command words.
Original languageEnglish
Pages (from-to)241-248
Number of pages8
JournalInternational Journal of Speech Technology
Issue number3
Publication statusPublished - 1 Jan 1998


  • Accumulative learning
  • Adaptive neural networks
  • Dynamic recognition neural network (DRNN)
  • Hidden markov model
  • Speech recognition

ASJC Scopus subject areas

  • Software
  • Language and Linguistics
  • Human-Computer Interaction
  • Linguistics and Language
  • Computer Vision and Pattern Recognition


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