Implementation of HMM and radial basis function for speech recognition

Author

Umarani, S.D. ; Raviram, P. ; Wahidabanu, R.S.D.

Author_Institution

Dept. of Electron. & Commun. Eng., Gov. Coll. of Eng., Salem, India

fYear

2009

fDate

22-24 July 2009

Firstpage

1

Lastpage

4

Abstract

The work aims at recognizing words from a continuous speech. To achieve this, cepstrum analysis of the speech signal is carried out. The speech signal is processed and the features are extracted using cepstrum analysis. The extracted features are given as inputs for the hidden Markov model (HMM) followed by training radial basis function (RBF). During the testing process, the words are separated and compared in the database. If a word matches then subsequent action is carried out. If the word is not present, then it is added to the database.

Keywords

cepstral analysis; hidden Markov models; radial basis function networks; speech recognition; cepstrum analysis; hidden Markov model; radial basis function; speech recognition; word recognition; Cepstral analysis; Cepstrum; Feature extraction; Hidden Markov models; Signal analysis; Signal processing; Speech analysis; Speech processing; Speech recognition; Testing; Hidden Markov model; Radial basis function; artificial neural network; cepstrum analysis; speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Agent & Multi-Agent Systems, 2009. IAMA 2009. International Conference on

Conference_Location

Chennai

Print_ISBN

978-1-4244-4710-7

Type

conf

DOI

10.1109/IAMA.2009.5228022

Filename

5228022