### Abstract

Original language | English |
---|---|

Title of host publication | Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing |

Editors | J Kwok, LM Po |

Place of Publication | Hong Kong |

Publisher | IEEE, Institute of Electrical and Electronics Engineers |

Pages | 141-144 |

Number of pages | 4 |

ISBN (Print) | 0-7803-8688-4 |

DOIs | |

Publication status | Published - 2004 |

Event | 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing - , Hong Kong Duration: 19 Oct 2004 → 21 Oct 2004 |

### Conference

Conference | 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing |
---|---|

Country | Hong Kong |

Period | 19/10/04 → 21/10/04 |

### Fingerprint

### Cite this

*Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing*(pp. 141-144). Hong Kong: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIMP.2004.1434020

}

*Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing.*IEEE, Institute of Electrical and Electronics Engineers, Hong Kong, pp. 141-144, 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, 19/10/04. https://doi.org/10.1109/ISIMP.2004.1434020

**Estimation of Prior Probabilities in Speaker Recognition.** / Tran, Dat.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

TY - GEN

T1 - Estimation of Prior Probabilities in Speaker Recognition

AU - Tran, Dat

PY - 2004

Y1 - 2004

N2 - According to Bayesian decision theory, the maximum a posteriori (MAP) decision rule is used to minimize the speaker recognition error rate. The a posteriori probability is determined if the a priori probability and the likelihood function are known. However, there has been no method to determine the a priori probability, therefore the maximum likelihood (ML) decision rule is used instead. The paper proposes a method to estimate the a priori probability for speakers based on a training data set and speaker models. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database using the MAP rule showed lower error rates than using the ML rule.

AB - According to Bayesian decision theory, the maximum a posteriori (MAP) decision rule is used to minimize the speaker recognition error rate. The a posteriori probability is determined if the a priori probability and the likelihood function are known. However, there has been no method to determine the a priori probability, therefore the maximum likelihood (ML) decision rule is used instead. The paper proposes a method to estimate the a priori probability for speakers based on a training data set and speaker models. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database using the MAP rule showed lower error rates than using the ML rule.

U2 - 10.1109/ISIMP.2004.1434020

DO - 10.1109/ISIMP.2004.1434020

M3 - Conference contribution

SN - 0-7803-8688-4

SP - 141

EP - 144

BT - Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing

A2 - Kwok, J

A2 - Po, LM

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Hong Kong

ER -