| Autor: |
Shrestha, Roman, Glackin, Cornelius, Wall, Julie, Cannings, Nigel, Rajwadi, Marvin, Kada, Satya, Laird, James, Laird, Thea, Woodruff, Chris |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
| DOI: |
10.5281/zenodo.7017134 |
| Popis: |
This paper presents a novel Variational Bayes x-vector Voice Print Extraction (VBxVPE) system, capable of capturing vocal variations using multiple x-vector representations with two-stage clustering and outlier detection for robust speaker recognition and verification. The presented approach demonstrates beyond the state-of-the-art results when evaluated against the ‘core-core’ and ‘core-multi’ evaluation conditions of the Speakers In the Wild dataset, achieving an Equal Error Rate of 1.06%, Cost of Detection score of 0.052, minimum Cost of Detection score of 0.010, Speaker Identification Accuracy of 95.84% with Precision, Recall and F1 score values of 0.964, 0.958 and 0.961, respectively on the ‘core-core’ evaluation condition and Equal Error Rate of 1.07%, Cost of Detection score of 0.066, minimum Cost of Detection score of 0.010 with Precision, Recall and F1 score values of 0.967, 0.963 and 0.965, respectively on the ‘core-multi’ evaluation condition. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
