4 citations · 9 across the 7 of their papers we have counts for
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eess.AS2023★ 1 cited
Towards generalisable and calibrated synthetic speech detection with self-supervised representations
Octavian Pascu, Adriana Stan, Dan Oneata +2
Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current…
eess.AS2023
An analysis on the effects of speaker embedding choice in non auto-regressive TTS
Adriana Stan, Johannah O'Mahony
In this paper we introduce a first attempt on understanding how a non-autoregressive factorised multi-speaker speech synthesis architecture exploits the information present in diff…
eess.AS2023★ 3 cited
Residual Information in Deep Speaker Embedding Architectures
Adriana Stan
Speaker embeddings represent a means to extract representative vectorial representations from a speech signal such that the representation pertains to the speaker identity alone. T…