We propose a scheme that improves the robustness of continuous HMM systems that use mixture observation densities by sharing the same mixture components among different HMM states. The sets of HMM states that share the same mixture components are determined automatically using agglomerative clustering techniques. Experimental results on the Wall-Street Journal Corpus show that our new form of output distributions achieves a 25% reduction in error rate over typical tied-mixture systems. >
['Vassilios Digalakis', 'Hy Murveit']
Genones: optimizing the degree of mixture tying in a large vocabulary hidden Markov model based speech recognizer