The design of a transport protocol for high precision industrial networks is a complex problem since the industrial applications are resource-critical, time-critical, session-less, and safety-critical. These requirements make existing end-to-end transport mechanisms unsuitable for use in the industry verticals that require control-systems type communication. This paper proposes a lightweight, connection-less, and reliable protocol called TinTin. It utilizes the in-network capabilities to compensate for end device constraints. The mechanisms adopted in TinTin aim to reduce the communication overheads associated with well-known connection-oriented protocols. This paper discusses the design considerations of TinTin in detail and presents an early stage proof-of-concept implementation of TinTin with New IP.

The performance of a speaker verification system is severely degraded by spoofing attacks generated from artificial speech synthesizers. Recently, several approaches have been proposed for classifying natural and synthetic speech (spoof detection) which can be used in conjunction with a speaker verification system. In this paper, we attempt to develop a joint modelling approach which can detect the presence of spoofing attacks while also performing the speaker verification task. We propose a factor modelling approach where the spoof variability subspace and the speaker variability subspace are jointly trained. The lower dimensional projections in these subspaces are used for speaker verification as well as spoof detection tasks. We also investigate the benefits of linear discriminant analysis (LDA), widely used in speaker recognition, for the spoof detection task. Several experiments are performed using the speaker and spoofing (SAS) database. For speaker verification, we compare the performance of the proposed method with a baseline method of fusing a conventional speaker verification system and a spoof detection system. In these experiments, the proposed approach provides substantial improvements for spoof detection (relative improvements of 20% in EER over the baseline) as well as speaker verification under spoofing conditions (relative improvements of 40% in EER over the baseline).