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We summarize the accomplishments of a multidisciplinary 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 pho-netic and lexical discovery, we consider unified evaluation metrics, present two new approaches for(More)
We present a new framework for the evaluation of speech representations in zero-resource settings, that extends and complements previous work by Carlin, Jansen and Hermansky [1]. In particular, we replace their Same/Different discrimination task by several Minimal-Pair ABX (MP-ABX) tasks. We explain the analytical advantages of this new framework and apply(More)
We show that it is possible to learn an efficient acoustic model using only a small amount of easily available word-level similarity annotations. In contrast to the detailed phonetic labeling required by classical speech recognition technologies, the only information our method requires are pairs of speech excerpts which are known to be similar (same word)(More)
The Interspeech 2015 Zero Resource Speech Challenge aims at discovering subword and word units from raw speech. The challenge provides the first unified and open source suite of evaluation metrics and data sets to compare and analyse the results of unsupervised linguistic unit discovery algorithms. It consists of two tracks. In the first, a psychophysically(More)
Infants learn language at an incredible speed, and one of the first steps in this voyage is learning the basic sound units of their native languages. It is widely thought that caregivers facilitate this task by hyperarticulating when speaking to their infants. Using state-of-the-art speech technology, we addressed this key theoretical question: Are sound(More)
We test both bottom-up and top-down approaches in learning the phonemic status of the sounds of English and Japanese. We used large corpora of spontaneous speech to provide the learner with an input that models both the linguistic properties and statistical regularities of each language. We found both approaches to help discriminate between allophonic and(More)
The Minimal-Pair ABX (MP-ABX) paradigm has been proposed as a method for evaluating speech features for zero-resource/unsupervised speech technologies. We apply it in a phoneme discrimination task on the Articulation Index corpus to evaluate the resistance to noise of various speech features. In Experiment 1, we evaluate the robustness to additive noise at(More)
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