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Cumulant GAN
In this paper, we propose a novel loss function for training Generative Adversarial Networks (GANs) aiming towards deeper theoretical understanding as well as improved performance for the underlying optimization problem.
Yannis Pantazis
,
Dipjyoti Paul
,
Michail Fasoulakis
,
Yannis Stylianou
,
Markos Katsoulakis
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Speech frame selection for spoofing detection with an application to partially spoofed audio-data
In this paper, we introduce a frame selection strategy for improved detection of spoofed speech.
A Kishore Kumar
,
Dipjyoti Paul
,
Monisankha Pal
,
Md Sahidullah
,
Goutam Saha
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Synthetic speech detection using fundamental frequency variation and spectral feature
In this paper, we targeted that by focusing on three major types of artifacts related to magnitude, phase and pitch variation, which are introduced during the generation of synthetic speech. We proposed a new approach to detect synthetic speech using score-level fusion of front-end features namely, constant Q cepstral coefficients (CQCCs), all-pole group delay function (APGDF) and fundamental frequency variation (FFV).
Monisankha Pal
,
Dipjyoti Paul
,
Goutam Saha
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Spectral Features for Synthetic Speech Detection
In this paper, we first study the characteristics of synthetic speech vis-à-vis natural speech and then propose a set of novel short-term spectral features that can efficiently capture the discriminative information between them.
Dipjyoti Paul
,
Monisankha Pal
,
Goutam Saha
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