Decision Tree-Based Acoustic Models for Speech Recognition with Improved Smoothness

This article proposes a new acoustic model using decision trees (DTs) as replacements for Gaussian mixture models (GMM) to compute the observation likelihoods for a given hidden Markov model state in a speech recognition system. DTs have a number of advantageous properties, such as that they do not impose restrictions on the number or types of features, and that they automatically perform feature selection. This article explores and exploits DTs for the purpose of large vocabulary speech recognition. Equal and decoding questions have newly been introduced into DTs to directly model genderand context-dependent acoustic space. Experimental results for the 5k ARPA wall-street-journal task show that context information significantly improves the performance of DT-based acoustic models as expected. Context-dependent DT-based models are highly compact compared to conventional GMM-based acoustic models. This means that the proposed models have effective data-sharing across various context classes.