Chinese Speech Recognition Based on Finite-State Radial Basis Function Networks

李苇营; 易克初; 吴文虎; 方棣棠

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Chinese Speech Recognition Based on Finite-State Radial Basis Function Networks

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- Chinese Speech Recognition Based on Finite-State Radial Basis Function Networks
- Acta Electronica Sinica Issue 1, (1996)
- 作者机构：
  
  1. 清华大学计算机系
  2. 西安电子科技大学信息工程系
  3. 清华大学计算机系西安电子科技大学信息工程系
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN912.34
- Published：1996
- 稿件说明：
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[1]李苇营,易克初,吴文虎,方棣棠.基于有限状态径向基函数网络的汉语语音识别研究[J].电子学报,1996(01):101-104.

李苇营, 易克初, 吴文虎, et al. Chinese Speech Recognition Based on Finite-State Radial Basis Function Networks[J]. Acta Electronica Sinica, 1996, (1).
[1]李苇营,易克初,吴文虎,方棣棠.基于有限状态径向基函数网络的汉语语音识别研究[J].电子学报,1996(01):101-104. DOI：

李苇营, 易克初, 吴文虎, et al. Chinese Speech Recognition Based on Finite-State Radial Basis Function Networks[J]. Acta Electronica Sinica, 1996, (1). DOI：

摘要

本文提出了有限状态径向基函数（ＦＳＲＢＦ）网络结构。它可用Ｋ－均值聚类算法和最小二乘算法分层独立训练，训练速度快。通过汉语语音识别实验，研究了ＦＳＲＢＦ网及子网的特性。结果表明，ＦＳＲＢＦ结构很适于处理时序信息，易于推广到其他识别单元的系统中。

Abstract

In this paper

Finite-State Radial Basis Function （FSRBF） network structure is proposed

which can be trained layer-by-layer by means of k-means clustering and LMS algorithm.The properties of FSRBF net and subnet are studied by Chinese digit recognition experiments. The results suggest that FSRBF is suitable for time series signal processing and can be generalized easily.

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