On segmentation and recognition of connected digits based on neural network model

Jhing Fa Wang, Chung-Hsien Wu, Ruey Chinq Shyu, Jau Yien Lee

Research output: Contribution to conferencePaperpeer-review

Abstract

Summary form only given, as follows. An automatic segmentation and recognition system based on a neural network model is proposed, and a backpropagation learning algorithm is employed to establish the networks. The main new idea for segmentation is to classify the energy, spectrum, and pitch-period transitions that occur at the boundaries between syllables. These feature transitions are used as the input patterns of the neural network . The segmented syllables are then used as the basic units for recognition by being fed them into the well-trained network. Ten digits (0-9) and syllables spoken in Mandarin are used in the speaker-independent phase for segmentation experiments, and only ten digits are used in the speaker-dependent phase for recognition experiments. With an average speaking rate of 150 digits per minute, a segmentation accuracy with a coincidence rate of 95.7% and recognition rate of 97.2% can be achieved.

Original languageEnglish
Number of pages1
Publication statusPublished - 1990 Dec 1
Event1990 IEEE International Symposium on Information Theory - San Diego, CA, USA
Duration: 1990 Jan 141990 Jan 19

Other

Other1990 IEEE International Symposium on Information Theory
CitySan Diego, CA, USA
Period90-01-1490-01-19

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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