Name
Abstract | ||
|---|---|---|
Recent advancements in voice conversion (VC) and speech synthesis research make speech-based biometric systems highly prone to spoofing attacks. This can provoke an increase in false acceptance rate in such systems and requires countermeasure to mitigate such spoofing attacks. In this paper, we first study the characteristics of synthetic speech vis-à-vis natural speech and then propose a set of n... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSTSP.2017.2684705 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Speech,Speech synthesis,Natural languages,Mel frequency cepstral coefficient,Feature extraction | Mel-frequency cepstrum,Speech synthesis,Pattern recognition,Spoofing attack,Computer science,Voice activity detection,Word error rate,Filter bank,Speech recognition,Synthetic data,Artificial intelligence,Mixture model | Journal |
Volume | Issue | ISSN |
11 | 4 | 1932-4553 |
Citations | PageRank | References |
10 | 0.53 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dipjyoti Paul | 1 | 21 | 3.76 |
| Monisankha Pal | 2 | 25 | 2.41 |
| Goutam Saha | 3 | 255 | 23.17 |