# Transparent pronunciation scoring using articulatorily weighted phoneme edit distance

## TL;DR

This paper introduces a transparent pronunciation scoring system for language learning that uses articulatorily weighted phoneme edit distance, enabling detailed feedback on mispronunciations and improving gamified pronunciation training.

## Contribution

It presents a novel white-box scoring model based on weighted Levenshtein edit distance that incorporates articulatory differences, enhancing interpretability over black box methods.

## Key findings

- The system provides human-readable feedback on mispronunciations.
- It outperforms established black box scoring methods in accuracy.
- The approach supports engaging pronunciation learning games.

## Abstract

For researching effects of gamification in foreign language learning for children in the "Say It Again, Kid!" project we developed a feedback paradigm that can drive gameplay in pronunciation learning games. We describe our scoring system based on the difference between a reference phone sequence and the output of a multilingual CTC phoneme recogniser. We present a white-box scoring model of mapped weighted Levenshtein edit distance between reference and error with error weights for articulatory differences computed from a training set of scored utterances. The system can produce a human-readable list of each detected mispronunciation's contribution to the utterance score. We compare our scoring method to established black box methods.