Phone duration modeling using gradient tree boosting

Phone duration modeling using gradient tree boosting

Boosting Material Modeling Using Game Tree Search

Composition optimization of material is common and costly problem. To avoid redundant measurements, it is effective to expect unknown result by considering previous results. However, the expectation is frequently wrong due to unexpected characteristics inside real materials and it causes local optimum trap. This study presents game tree search can escape from local optimum by limiting the spati...

Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Mobile phone customers face many choices regarding handset hardware, add-on services, and features to subscribe to from their service providers. Mobile phone companies are now increasingly interested in the drivers of migration to third generation (3G) hardware and services. Using real world data provided to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Da...

Phone Duration Modeling for LVCSR Using Neural Networks

We describe our work on incorporating probabilities of phone durations, learned by a neural net, into an ASR system. Phone durations are incorporated via lattice rescoring. The input features are derived from the phone identities of a context window of phones, plus the durations of preceding phones within that window. Unlike some previous work, our network outputs the probability of different d...

Phone duration modeling using clustering of rich contexts

This paper describes a phone duration model applied to speech recognition. The model is based on a decision tree that finds clusters of phones in various contexts that tend to have similar durations. Wide contexts with rich linguistic and phonetic features are used. To better model varying and non-stationary speaking rates, the contextual features also include the observed duration values of pr...