Browsing by Author "King, S."

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SAS : A speaker verification spoofing database containing diverse attacks
(IEEE, 2015) Wu, Z.; Khodabakhsh, Ali; Demiroğlu, Cenk; Yamagishi, J.; Saito, D.; Toda, T.; King, S.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Khodabakhsh, Ali
This paper presents the first version of a speaker verification spoofing and anti-spoofing database, named SAS corpus. The corpus includes nine spoofing techniques, two of which are speech synthesis, and seven are voice conversion. We design two protocols, one for standard speaker verification evaluation, and the other for producing spoofing materials. Hence, they allow the speech synthesis community to produce spoofing materials incrementally without knowledge of speaker verification spoofing and anti-spoofing. To provide a set of preliminary results, we conducted speaker verification experiments using two state-of-the-art systems. Without any anti-spoofing techniques, the two systems are extremely vulnerable to the spoofing attacks implemented in our SAS corpus.
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Using eigenvoices and nearest-neighbors in HMM-based cross-lingual speaker adaptation with limited data
(IEEE, 2017-04) Sarfjoo, Seyyed Saeed; Demiroğlu, Cenk; King, S.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Sarfjoo, Seyyed Saeed
Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. To that end, we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish-English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping, which performed significantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intralingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system.