Name
Abstract | ||
|---|---|---|
The paper presents a speaker detection system based on phoneme specific hidden Markov model in combination with Gaussian mixture model. Our motivation stems from the fact that the phoneme specific HMM system can model temporal variations and provides possibility to ponder the scores of specific phonemes as well as efficient pruning. The performance of the system has been evaluated on speech database which contains utterances in Serbian from 250 speakers (10 of them being the target speakers). The proposed model is compared to a system based on Gaussian mixture model - universal background model, and showed a significant improvement in detection performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11581-8_51 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Speaker detection,Hidden Markov models,Gaussian mixture models | Pattern recognition,Computer science,Markov model,Speech recognition,Artificial intelligence,Speaker detection,Hidden Markov model,Mixture model | Conference |
Volume | ISSN | Citations |
8773 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edvin Pakoci | 1 | 10 | 3.02 |
| Nikša Jakovljević | 2 | 28 | 4.11 |
| Branislav M. Popovic | 3 | 96 | 17.13 |
| Dragisa Miskovic | 4 | 11 | 3.19 |
| Darko Pekar | 5 | 37 | 8.28 |