Rosa González Hautamäki

TL;DR: A new database designed to support the development of replay spoofing countermeasures tailored towards the protection of text-dependent ASV systems from replay attacks in the face of variable recording and playback conditions, derived from the re-recording of the original RedDots database.

TL;DR: This work studies the vulnerability of two well-known speaker recognition systems, traditional Gaussian mixture model – universal background model (GMM-UBM) and a state-of-the-art i-vector classifier with cosine scoring, which consists of one professional Finnish imitator impersonating five wellknown Finnish public figures.

TL;DR: The performance of the human listening panel shows that imitated speech increases the difficulty of the speaker verification task, and a statistically significant association is found between listener accuracy and self reported factors only when familiar voices were present in the test.

TL;DR: This paper investigates the possibility and methods of attacking a TM-based ASV system, followed by a pilot data collection, and studies the use of spectral features for VLD using both single-channel and dual-channel ASV systems.

TL;DR: This work approaches voice disguise from the perspective of acoustical and perceptual analysis using a self-collected corpus of 60 native Finnish speakers producing utterances in normal, intended young and intended old voice modes, and investigates whether or not speaker comparisons that are deemed easy or difficult by a modern ASV system have a similar difficulty level for the human listeners.