@MASTERSTHESIS\{IMM2004-03319,
    author       = "L. Feng",
    title        = "Speaker Recognition",
    year         = "2004",
    keywords     = "feature extraction, {MFCC,} {KNN,} speaker pruning, {DDHMM,} speaker recognition and {ELSDSR}",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Prof. Lars Kai Hansen",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3319-full.html",
    abstract     = "The work leading to this thesis has been focused on establishing a text-independent closed-set speaker recognition system. Contrary to other recognition systems, this system was built with two parts for the purpose of improving the recognition accuracy. The first part is the speaker pruning performed by {KNN} algorithm. To decrease the gender misclassification in {KNN,} a novel technique was used, where Pitch and {MFCC} features were combined. This technique, in fact, does not only improve the gender misclassification, but also leads to an increase on the total performance of the pruning. The second part is the {DDHMM} speaker recognition performed on the  survived  speakers after pruning. By adding the speaker pruning part, the system recognition accuracy was increased 9.3\%.

During the project period, an English Language Speech Database for Speaker Recognition (ELSDSR) was built. The system was trained and tested with both {TIMIT} and {ELSDSR} database."
}