On the selection of prosodic features for language modeling

Alejandro Vega, University of Texas at El Paso


Previous studies show that immediate and long range prosodic context provide beneficial information when applied to a language model. However, the fact that some features provide more information to the prediction task should be considered. If the information contribution of each feature can be determined, then a well-crafted feature set can be built to improve the performance of a language model. In this study, I measure the contribution of different prosodic features to a baseline trigram model. Using this information, it should be possible to build a language model that uses the most informative resources and ultimately performs better than a language model that includes prosodic information naively. Using this information, I build a prosodic feature set of 103 prosodic features from past and future context computed for both speaker and interlocutor. Principal component analysis is applied to this feature set to build a model that achieves a 25.9% perplexity reduction relative to a tri-gram model. However, this model falls short of performance improvements achieved by a similar model without proper feature selection by −1.2%.

Subject Area

Computer science

Recommended Citation

Vega, Alejandro, "On the selection of prosodic features for language modeling" (2012). ETD Collection for University of Texas, El Paso. AAI1533257.