Spoken Pass-Phrase Verification in the i-vector Space
| Autor: | Hossein Zeinali, Hossein Sameti, Honza Cernocky, Lukas Burget |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Computer Science - Computation and Language Phrase Audio and Speech Processing (eess.AS) Computer science Speech recognition FOS: Electrical engineering electronic engineering information engineering I vector Space (mathematics) Computation and Language (cs.CL) Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | Odyssey |
| DOI: | 10.21437/odyssey.2018-52 |
| Popis: | The task of spoken pass-phrase verification is to decide whether a test utterance contains the same phrase as given enrollment utterances. Beside other applications, pass-phrase verification can complement an independent speaker verification subsystem in text-dependent speaker verification. It can also be used for liveness detection by verifying that the user is able to correctly respond to a randomly prompted phrase. In this paper, we build on our previous work on i-vector based text-dependent speaker verification, where we have shown that i-vectors extracted using phrase specific Hidden Markov Models (HMMs) or using Deep Neural Network (DNN) based bottle-neck (BN) features help to reject utterances with wrong pass-phrases. We apply the same i-vector extraction techniques to the stand-alone task of speaker-independent spoken pass-phrase classification and verification. The experiments on RSR2015 and RedDots databases show that very simple scoring techniques (e.g. cosine distance scoring) applied to such i-vectors can provide results superior to those previously published on the same data. |
| Databáze: | OpenAIRE |
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