Modeling Probabilistic Reduction using Information Theory and Naive Discriminative Learning
This work addresses modeling acoustic reduction in linguistics, but it is incremental as it compares existing methods on a specific dataset.
This study compared probabilistic predictors based on information theory and Naive Discriminative Learning (NDL) in modeling acoustic word duration, finding that an N-gram model outperformed NDL models, challenging assumptions about NDL's cognitive effectiveness, though incorporating information theory improved NDL performance.
This study compares probabilistic predictors based on information theory with Naive Discriminative Learning (NDL) predictors in modeling acoustic word duration, focusing on probabilistic reduction. We examine three models using the Buckeye corpus: one with NDL-derived predictors using information-theoretic formulas, one with traditional NDL predictors, and one with N-gram probabilistic predictors. Results show that the N-gram model outperforms both NDL models, challenging the assumption that NDL is more effective due to its cognitive motivation. However, incorporating information-theoretic formulas into NDL improves model performance over the traditional model. This research highlights a) the need to incorporate not only frequency and contextual predictability but also average contextual predictability, and b) the importance of combining information-theoretic metrics of predictability and information derived from discriminative learning in modeling acoustic reduction.