Predicting unseen triphones with senones

In large-vocabulary speech recognition, there are always new triphones that are not covered in the training data. These unseen triphones are usually represented by corresponding diphones or context-independent monophones. It is proposed that decision-tree-based senones be used to generate needed senonic baseforms for unseen triphones. A decision tree is built for each individual Markov state of each phone, and the leaves of the trees constitute the senone codebook. A Markov state of any triphone traverses the corresponding tree until it reaches a leaf to find the senone it is to be associated with. The DARPA 5000-word peaker-independent Wall Street Journal dictation task is used to evaluate the proposed method. The word error rate is reduced by more than 10% when unseen triphones are modeled by the decision-tree-based senones.<>