An investigation of tied-mixture GMM based triphone state clustering

Parameter tying is a crucial scheme for robust context dependent acoustic modeling since it takes a major role in balancing the desired model complexity and the amount of data available. In this paper, a modified decision tree state clustering scheme based on tied-mixture Gaussian Mixture Model (GMM) is proposed. Instead of using a single Gaussian untied triphone system, a tied-mixture GMM triphone system is adopted as a better acoustic model for state clustering. Meanwhile, the proposed scheme allows easy incorporation of discriminative training during clustering. Experimental results show that for a varying number of state clusters, the proposed approach consistently outperforms the standard single Gaussian based state tying. The best WER performance has a 10.5% relative improvement over the conventional decision tree clustering and the proposed scheme achieves its best performance using a much smaller number of state clusters. Moreover, detailed analyses reveal that the proposed GMM clustering has a better state distribution which leads to 1) better frame-state alignments 2) better phonetic question selections. These two factors may make the proposed approach superior for clustering.