Improving decision trees for acoustic modeling

In the last few years, the power and simplicity of classification trees as acoustic modeling tools have gained them much popularity. In 1995, the authors studied “tree units”, which cluster parameters at the HMM level. Building on this earlier work, they examine some new variants of Young et al.´s (1994) “tree states”, which cluster parameters at the state level. They have experimented with: 1. Making unitary models (which contain additional information about the context); 2. Pruning trees with various severity levels (idea introduced in [1]); 3. Pooling some leaves (idea adapted from Young et al.); 4. Refining the questions; 5. Questions about the position of the phone within the word; 6. Lookahead search; 7. Making a single tree for each phone