ChildAugment: Data augmentation methods for zero-resource children's speaker verification.

The accuracy of modern automatic speaker verification (ASV) systems, when trained exclusively on adult data, drops substantially when applied to children's speech. The scarcity of children's speech corpora hinders fine-tuning ASV systems for children's speech. Hence, there is a timely need to explore more effective ways of reusing adults' speech data.

t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators.

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric verification, they address different detection tasks and are hence typically evaluated separately. Evidence shows that this approach is suboptimal.

On the limits of automatic speaker verification: Explaining degraded recognizer scores through acoustic changes resulting from voice disguise.

In speaker verification research, objective performance benchmarking of listeners and automatic speaker verification (ASV) systems are of key importance in understanding the limits of speaker recognition. While the adoption of common data and metrics has been instrumental to progress in ASV, there are two major shortcomings. First, the utterances lack intentional voice changes imposed by the speaker.