Improvement of the learning process of the automated speaker recognition system for critical use with HMM-DNN component
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Bykov Mykola M., Kovtun Viacheslav V. , Kobylyanska Iryna M., Wójcik Waldemar, Smailova Saule |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 588 - 597 |
| Web of Science® Times Cited: | 3 |
| Scopus® Cytowania: | 3 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | XLIV-th IEEE-SPIE Joint Symposium on Photonics, Web Engineering, Electronics for Astronomy and High Energy Physics Experiments |
| Skrócona nazwa konferencji: | XLIV SPIE-IEEE-PSP 2019 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 26 maja 2019 do 2 czerwca 2019 |
| Miasto konferencji: | Wilga |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The article presents the results of the adaptation of the hybrid HMM-DNN speech synthesis model for use in automated speaker recognition system for critical use (ASRSCU). In particular, the process of learning the HMM-DNN speech synthesis model with the estimation of the difference between the posterior probability distributions of all HMM states and the actual a posteriori probability distribution, calculated by DNN, and the use of semantic information in the speaker recognition process, has been improved. The features that are observed in the sequence of frames to which the input phonogram is divided describe this information. The obtained results allowed improving the efficiency of the textdependent speaker recognition when using ASRSCU in a noisy acoustic environment. The article formulated measures for the structural integration of the HMM-DNN component in ASRSCU and describes the practical aspects of this process. In particular, the choice of the type and the method of normalization of the vectors of basic informative features at the frame level was substantiated, the number of HMM states and GMM parameters were determined depending on the parameters of the chosen formation model, and the procedure for interpreting the recognition results was described. The paper formulates measures to optimize the learning process of the ASRSCU with the HMM-DNN component, which will be exploited in noisy environments. |
