Prosodic Word Prediction Using a Maximum Entropy Approach

Abstract

As the basic prosodic unit, the prosodic word influences the naturalness and the intelligibility greatly. Although the research shows that the lexicon word are greatly different from the prosodic word, the lexicon word still provides the important cues for the prosodic word forming. The rhythm constraint is another important factor for the prosodic word prediction. Some lexicon word length patterns trend to be combined together. Based on the mapping relationship and the difference between the lexicon words and the prosodic words, the process of the prosodic word prediction is divided into two parts, grouping the lexicon word to the prosodic word and splitting the lexicon word into prosodic words. This paper proposes a maximum entropy method to model these two parts, respectively. The experiment results show that this maximum entropy model is competent for the prosodic word prediction task. In the word grouping model, a feature selection algorithm is used to induce more efficient features for the model, which not only decrease the feature number greatly, but also improve the model performance at the same time. And, the splitting model can correctly detect the prosodic word boundary in the lexicon word. The f-score of the prosodic word boundary prediction reaches 95.55%.KeywordsMaximum EntropyStatistic Machine TranslationLexical InformationWord GroupingMaximum Entropy ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.