We describe a new challenge aimed at discovering subword and word units from raw speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It aims at constructing systems that generalize across languages and adapt to new speakers. The design features and evaluation metrics of the challenge are presented and the results of seventeen models are discussed.Index Terms-zero resource speech technology, subword modeling, acoustic unit discovery, unsupervised term discovery
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.
Theropithecus gelada, Papio anubis and Cercopithecus aethiops are commonly sympatric in Ethiopia. It is suggested that niche separation would be more marked among terrestrial open country species than among forest primates. The ecological relationships between these three species in an Ethiopian valley where they coexist are analysed. Quantitative data are presented on density and biomass, size of home ranges and day ranges, activity patterns, use of habitat, diet and feeding patterns and on interspecific interactions. These are compared across the species to determine to what extent ecological competition could occur and in what ways it is reduced. The data are discussed with reference to studies of forest primate communities where niche overlap has commonly been reported.
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