We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal identity) of a source speaker to a target speaker while maintaining linguistic information. As an update to the previous challenge, we considered both parallel and nonparallel data to form the Hub and Spoke tasks, respectively. A total of 23 teams from around the world submitted their systems, 11 of them additionally participated in the optional Spoke task. A large-scale crowdsourced perceptual evaluation was then carried out to rate the submitted converted speech in terms of naturalness and similarity to the target speaker identity. In this paper, we present a brief summary of the state-of-the-art techniques for VC, followed by a detailed explanation of the challenge tasks and the results that were obtained. Voice Conversion Basic Framework for Speaker ConversionTo achieve speaker conversion, a data-driven approach is usually used to develop a conversion function to modify speech features including both segmental and prosodic features. Two main VC frameworks have been studied on the basis of the datadriven approach, parallel VC and non-parallel VC.Parallel VC is the most standard framework. A parallel arXiv:1804.04262v1 [eess.AS]

This paper describes the Voice Conversion Challenge 2016 devised by the authors to better understand different voice conversion (VC) techniques by comparing their performance on a common dataset. The task of the challenge was speaker conversion, i.e., to transform the voice identity of a source speaker into that of a target speaker while preserving the linguistic content. Using a common dataset consisting of 162 utterances for training and 54 utterances for evaluation from each of 5 source and 5 target speakers, 17 groups working in VC around the world developed their own VC systems for every combination of the source and target speakers, i.e., 25 systems in total, and generated voice samples converted by the developed systems. These samples were evaluated in terms of target speaker similarity and naturalness by 200 listeners in a controlled environment. This paper summarizes the design of the challenge, its result, and a future plan to share views about unsolved problems and challenges faced by the current VC techniques.

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Salmonella enterica is one of the most important foodborne pathogens around the world. In the last years, S . enterica serovar Infantis has become an important emerging pathogen in many countries, often as multidrug resistant clones. To understand the importance of S. enterica in the broiler industry in Ecuador, we performed a study based on phenotypic and WGS data of isolates from poultry farms, chicken carcasses and humans. We showed a high prevalence of S. enterica in poultry farms (41.4%) and chicken carcasses (55.5%), but a low prevalence (1.98%) in human samples. S . Infantis was shown to be the most prevalent serovar with a 98.2, 97.8, and 50% in farms, foods, and humans, respectively, presenting multidrug resistant patterns. All sequenced S . Infantis isolates belonged to ST32. For the first time, a pESI-related megaplasmid was identified in Ecuadorian samples. This plasmid contains genes of antimicrobial resistance, virulence factors, and environmental stress tolerance. Genomic analysis showed a low divergence of S . Infantis strains in the three analyzed components. The results from this study provide important information about genetic elements that may help understand the molecular epidemiology of S . Infantis in Ecuador.

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This paper presents an algorithm for detecting spoofing attacks against automatic speaker verification (ASV) systems. While such systems now have performances comparable to those of other biometric modalities, spoofing techniques used against them have progressed drastically. Several techniques can be used to generate spoofing materials (e.g., speech synthesis and voice conversion techniques), and detecting them only on the basis of differences at an acoustic speaker modeling level is a challenging task. Moreover, differences between "live" and artificially generated material are expected to gradually decrease in the near future due to advances in synthesis technologies. A previously proposed "voice liveness" detection framework aimed at validating whether speech signals were generated by a person or artificially created uses elementary algorithms to detect pop noise. Detection is taken as evidence of liveness. A more advanced detection algorithm has now been developed that combines single-and double-channel pop noise detection. Experiments demonstrated that this tandem algorithm detects pop noise more effectively: the detection error rate was up to 80% less that those achieved with the elementary algorithms.

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