Parametric feature-based voice recognition system using artificial neural network

M. Bodrzzaman*, K. Kuah, T. Jamil, C. Wang, X. Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, a speaker identification experiment was conducted using an artificial neural network. The speech data were collected from nine different speakers saying the same word ″Hello″. The speech data were then preprocessed for signal conditioning. A total of 14 feature parameters were obtained in which twelve of them are the coefficient of the 12th order linear predictor (LPC) and the other two were selected as the peak and bandwidth of spectral envelop. These 14 feature parameters then served as the input to the neural network for speaker classification. A standard two-layer feedforward neural network was trained to identify different feature sets associated with the corresponding speakers. The network size was selected to be 14-8-4 (14 input, 8 hidden and 4 output units). Nine (9) utterances from each speaker were used as training data and the other one served as testing data. No pagination in original publication.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE SOUTHEASTCON
PublisherPubl by IEEE
ISBN (Print)0780312570
Publication statusPublished - 1993
Externally publishedYes
EventProceedings of the IEEE Southeastcon '93 - Charlotte, NC, USA
Duration: Apr 4 1993Apr 7 1993

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
ISSN (Print)0734-7502


OtherProceedings of the IEEE Southeastcon '93
CityCharlotte, NC, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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