This paper investigates speaker identification with implicit extraction of speaker characteristics relevant to discrimination from the log power spectrum of training speech by employing the inductive power of dual Penalized Logistic Regression Machine (dPLRM). The dPLRM is one of kernel methods like the support vector machine (SVM) and has the inductive power due to the mechanism of the kernel regression. In text-independent speaker identification experiments with training speech uttered by 10 male speakers in three different sessions, we compares the performances of dPLRM, SVM and Gaussian mixture model (GMM)-based methods and show that dPLRM implicitly and effectively extracts speaker characteristics from the log power spectrum. It is also shown that dPLRM outperforms the other methods especially when the amount of training data is small.
Cite as: Matsui, T., Tanabe, K. (2005) dPLRM-based speaker identification with log power spectrum. Proc. Interspeech 2005, 2017-2020, doi: 10.21437/Interspeech.2005-632
@inproceedings{matsui05_interspeech,
author={Tomoko Matsui and Kunio Tanabe},
title={{dPLRM-based speaker identification with log power spectrum}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2017--2020},
doi={10.21437/Interspeech.2005-632}
}