- Linchen Zhu, A. Waibel. 2015. GMM free ASR using DNN based Cluster Trees. Abstract: Deep neural networks (DNN), as a kind of statistical models, have shown their superiority over Gaussian Mixture Models (GMM) in acoustic modeling when they are used to estimate the emission distributions of HMM states. However a standard DNN-HMM hybrid system is still reliant on GMMs in two aspects initial training alignments and cluster tree building. Recent work has shown that the training of a context independent DNN can be flat started without the initial alignments generated by a trained GMM-HMM system. In this work we propose a novel GMM-free approach for phonetic cluster tree building. To do this a context independent DNN is trained first. From this the average CI-DNN output for each polyphone state is calculated by passing the training samples through the CI-DNN, the results from which can then be used to measure the entropy distance between two sets of polyphone states in the clustering process. Experiments are performed to realize our novel approach, and in addition, cluster trees of different sizes are built to investigate the best performance of our novel approach. All implemented systems are tested on a test database, the test results show that DNN based cluster trees outperform GMM based trees when the number of leaves is greater than 12k, and that both approaches achieve their best performance at 18k leaves.
