Abstract
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 language | English |
|---|---|
| Title of host publication | Conference Proceedings - IEEE SOUTHEASTCON |
| Publisher | Publ by IEEE |
| ISBN (Print) | 0780312570 |
| Publication status | Published - 1993 |
| Event | Proceedings of the IEEE Southeastcon '93 - Charlotte, NC, USA Duration: Apr 4 1993 → Apr 7 1993 |
Other
| Other | Proceedings of the IEEE Southeastcon '93 |
|---|---|
| City | Charlotte, NC, USA |
| Period | 4/4/93 → 4/7/93 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Parametric feature-based voice recognition system using artificial neural network. / Bodrzzaman, M.; Kuah, K.; Jamil, T.; Wang, C.; Li, X.
Conference Proceedings - IEEE SOUTHEASTCON. Publ by IEEE, 1993.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Parametric feature-based voice recognition system using artificial neural network
AU - Bodrzzaman, M.
AU - Kuah, K.
AU - Jamil, T.
AU - Wang, C.
AU - Li, X.
PY - 1993
Y1 - 1993
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0027151282&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027151282
SN - 0780312570
BT - Conference Proceedings - IEEE SOUTHEASTCON
PB - Publ by IEEE
ER -