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This paper summarizes the work done by the authors for the Zero ResourceSpeech Challenge organized in the technical program of Interspeech 2015. Thegoal of the challenge is to discover linguistic units directly from unlabeledspeech data. The Multi-layered Acoustic Tokenizer (MAT) proposed in this workautomatically discovers multiple sets of acoustic tokens from the given corpus.Each acoustic token set is specified by a set of hyperparameters that describethe model configuration. These sets of acoustic tokens carry differentcharacteristics of the given corpus and the language behind thus can bemutually reinforced. The multiple sets of token labels are then used as thetargets of a Multi-target DNN (MDNN) trained on low-level acoustic features.Bottleneck features extracted from the MDNN are used as feedback for the MATand the MDNN itself. We call this iterative system the Multi-layered AcousticTokenizing Deep Neural Network (MAT-DNN) which generates high quality featuresfor track 1 of the challenge and acoustic tokens for track 2 of the challenge.

A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for  Unsupervised Discovery of Linguistic Units and Generation of High Quality  Features