用信息论改进判别学习模型，更好预测语音中的省略现象
Modeling Probabilistic Reduction using Information Theory and Naive Discriminative Learning
- 将信息论公式融入判别学习，提升语音省略建模能力
- N-gram模型在语音时长预测上优于两种判别学习模型
- 适合关注语音省略机制与认知计算的语音研究者
本研究比较基于信息论的概率预测器与朴素判别学习（NDL）预测器在建模语音词时长和概率省略方面的表现。使用Buckeye语料库，评估三种模型：基于信息论公式的NDL预测器、传统NDL预测器和N-gram概率预测器。结果表明，N-gram模型在预测性能上优于两个NDL模型，挑战了NDL因具认知动机而更优的假设。但将信息论公式引入NDL可显著提升其性能。研究指出：一、建模需同时考虑频率、上下文可预测性及平均上下文可预测性；二、结合信息论的可预测性度量与判别学习所得信息，对建模语音省略至关重要。
原文摘要 · Abstract (English)
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.
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