Beta 1

Title Speaker Recognition
Author Feng, Ling (Danmarks Tekniske Universitet, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2004
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.
Note Supervised by Prof. Lars Kai Hansen
Keywords feature extraction; MFCC; KNN; speaker pruning; DDHMM; speaker recognition and ELSDSR
Original PDF imm3319.pdf (1.70 MB)
Admin Creation date: 2006-06-22    Update date: 2008-03-17    Source: dtu    ID: 154747    Original MXD