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Gaussian free cluster tree construction using deep neural network

This paper presents a Gaussian free approach to constructing the cluster tree (CT) that context dependent acoustic models (CDAM) depend on. Over the last few years deep neural networks (DNN) have supplanted Gaussian mixture models (GMM) as the default method for acoustic modeling (AM). DNN AMs have also been successfully used to flat start context independent (CI) AMs and generate alignments on which CTs can be trained. Those approaches however still required Gaussians to build their CTs. Our proposed Gaussian free CT algorithm eliminates this requirements and allows, for the first time, the flat start training of state of the art DNN AMs without the use of Gaussian. An evaluation on the IWSLT transcription task demonstrates the effectiveness of this approach.