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MLP Analysis

This study explores the performance of various MLP (Multilayer Perceptron) models, including Canonical MLP, LL MLP, XC MLP, and their frame accuracy metrics when applied to word transitions in language models. We specifically examine the models' accuracy against chance accuracy while removing silence from the analysis. The focus is on cross-word asynchrony and the ability to handle different pronunciation variants and phonetic features occurring within a word or at most one word apart. This work aims to elucidate differences in state transitions across different MLP frameworks.

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MLP Analysis

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  1. MLP Analysis MLP vs Canonical MLP vs LL MLP vs XC

  2. MLP Frame Accuracy

  3. MLP Chance Accuracy

  4. MLP Frame Accuracy – Silence Removed

  5. MLP Chance Accuracy – Silence Removed

  6. Cross-wordAsynchronyModel wordUpdate word totalSubWordStates LM→ LTGSync nextWord LTSync nextWordState[LTG] wordTransition[LTG] stateTransition[LTG] subTwoWordState[LTG] subWordState[LTG] phoneState[LTG] [LTG] (differences from actual: pronunciation variants, features at most one word apart)

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