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Noise-Robust Speech Recognition Based on LPMCC Feature and RBF Neural Network

  • Hou XuemeiEmail author
  • Li Xiaolir
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

To solve the problem that recognition rates of speech recognition systems decrease in the noisy environment presently, the Linear Predictive Mel cepstrum coefficient (LPMCC) is used as feature parameter and uses character possessing LPMCC and RBF neural network which have optimal approach capability and the fast training speed, adopts clustering algorithm and entire-supervised algorithm and realizes a noise-robust speech recognition system based on RBF neural net-work. The hidden layer training of clustering algorithm used K-means clustering algorithm and output layer learning used linear least mean square. The adjustment of the entire parameters of entire-supervised algorithm is based on grads decline method. It is a kind of supervised learning algorithm and can choose excellent parameters. Experiments show that entire-supervised algorithm have higher recognition rates in different SNRs than clustering algorithm.

Keywords

Speech recognition RBF neural network LPCMCC Clustering algorithm Entire-supervised algorithm 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information EngineeringChang’an UniversityXi’anPeople’s Republic of China
  2. 2.College of AutomationXi’an University of Posts and TelecommunicationsXi’anChina

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