@InProceedings{Král2010_563,
author = {Pavel Král and Václav Matoušek},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2010},
title = {Evaluation of Automatic Speaker Recognition Approaches},
year = {2010},
editor = {Hansjörg Mixdorff},
month = mar,
pages = {228--233},
publisher = {TUDpress, Dresden},
abstract = {This paper deals with automatic speech recognition in Czech. We focus
here on context independent speaker recognition with a closed set of speakers. To the
best of our knowledge, there is no comparative study about different speaker
recognition approaches on the Czech language. The main goal of this paper is thus to
evaluate and compare several parametrization/classification methods in order to build
an efficient Czech speaker recognition system. All experiments are performed on
a Czech speaker corpus that contains approximately half one hour of speech from ten
Czech native speakers. Four parameterizations, which are mentioned in other studies
as particularly successful for the speaker recognition task, are compared: MEL
Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction Coefficients
(PLPC), Linear Prediction Reflection Coefficients (LPREFC) and Linear Prediction
Cepstral Coefficients (LPCEPSTRA). Two classifiers are compared: Hidden Markov
Models (HMMs) and Multi-Layer Perceptron (MLP). In this work, we further study
the impact of varying sizes of training corpus and test sentence on the recognition
accuracy for different parametrizations and classifiers. For instance, we
experimentally found that the recognition is still very accurate for test utterances as
short as two seconds. The best recognition accuracy is obtained with LPCEPSTRA/
LPREFC parametrizations and HMM classifier.},
isbn = {978-3-941298-85-9},
issn = {0940-6832},
keywords = {Speech Recognition},
url = {https://www.essv.de/pdf/2010_228_233.pdf},
}