31 citations · 48 across the 7 of their papers we have counts for
7 papers
Neural Encoding Detection is Not All You Need for Synthetic Speech Detection
Luca Cuccovillo, Xin Wang, Milica Gerhardt +1
This paper reviews the current state and emerging trends in synthetic speech detection. It outlines the main data-driven approaches, discusses the advantages and drawbacks of focus…
Multi-Task Transformer for Explainable Speech Deepfake Detection via Formant Modeling
Viola Negroni, Luca Cuccovillo, Paolo Bestagini +2
In this work, we introduce a multi-task transformer for speech deepfake detection, capable of predicting formant trajectories and voicing patterns over time, ultimately classifying…
Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol
Konstantinos Apostolidis, Jakob Abesser, Luca Cuccovillo +1
This paper presents a baseline approach and an experimental protocol for a specific content verification problem: detecting discrepancies between the audio and video modalities in…
Environment Classification via Blind Roomprints Estimation
Malte Baum, Luca Cuccovillo, Artem Yaroshchuk +1
In this paper we present a novel approach for environment classification for speech recordings, which does not require the selection of decaying reverberation tails. It is based on…
Open Challenges in Synthetic Speech Detection
Luca Cuccovillo, Christoforos Papastergiopoulos, Anastasios Vafeiadis +4
In this paper the current status and open challenges of synthetic speech detection are addressed. The work comprises an initial analysis of available open datasets and of existing…
Speaker-Independent Microphone Identification in Noisy Conditions
Antonio Giganti, Luca Cuccovillo, Paolo Bestagini +2
This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks…
Spectral Denoising for Microphone Classification
L. Cuccovillo, A. Giganti, P. Bestagini +2
In this paper, we propose the use of denoising for microphone classification, to enable its usage for several key application domains that involve noisy conditions. We describe the…