Biblio

In this paper, we propose a scheme to employ an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on a novel client-side embedding technique that is able to transmit a binary fingerprint. This enables secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using non-blind decoding, and it is robust with respect to common attacks. The proposed scheme can be a valid solution to both customer's rights and scalability issues in multimedia content distribution.

This work presents a novel method to estimate natural expressed emotions in speech through binary acoustic modeling. Standard acoustic features are mapped to a binary value representation and a support vector regression model is used to correlate them with the three-continuous emotional dimensions. Three different sets of speech features, two based on spectral parameters and one on prosody are compared on the VAM corpus, a set of spontaneous dialogues from a German TV talk-show. The regression analysis, in terms of correlation coefficient and mean absolute error, show that the binary key modeling is able to successfully capture speaker emotion characteristics. The proposed algorithm obtains comparable results to those reported on the literature while it relies on a much smaller set of acoustic descriptors. Furthermore, we also report on preliminary results based on the combination of the binary models, which brings further performance improvements.