Dealing with Cumulative Phonetic Alignment Drift in CTC-based Quranic Recitation Correction System

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Summary

Cumulative Phonetic Alignment Drift in a CTC-based Quranic recitation correction system causes phantom errors and accuracy drops in long Ayahs (>20 words). The issue arises from imperfect word boundaries in CTC models and the complexities of connected speech (Wasl) in Quranic recitation.

Root Cause

  • CTC Model Limitations: Lack of precise word boundaries leads to alignment challenges.
  • Connected Speech (Wasl): Phoneme merging or dropping (e.g., Ighdam, Hamzatul Wasl) complicates alignment.
  • Global Alignment Drift: Errors in early words propagate, causing later words to map incorrectly.

Why This Happens in Real Systems

  • Long Sequences: Longer Ayahs amplify alignment drift due to cumulative errors.
  • Weighted Proportional Mapping: Current logic fails to account for phonetic variations in connected speech.
  • SequenceMatcher Limitations: difflib.SequenceMatcher struggles with dynamic phonetic shifts.

Real-World Impact

  • Accuracy Drop: System accuracy falls below 5% in long Ayahs.
  • Phantom Errors: Incorrect mappings lead to false negatives/positives in recitation feedback.
  • User Frustration: Inaccurate corrections degrade user trust in the system.

Example or Code (if necessary and relevant)

from difflib import SequenceMatcher

def align_phonemes(predicted, reference):
    matcher = SequenceMatcher(None, predicted, reference)
    return matcher.get_matching_blocks()

How Senior Engineers Fix It

  • Local Alignment: Use dynamic programming (e.g., Needleman-Wunsch) for local phonetic alignment.
  • Phonetic Boundary Detection: Incorporate a secondary model to detect word boundaries in CTC outputs.
  • Context-Aware Mapping: Adjust alignment based on phonetic rules of Quranic recitation (e.g., Wasl handling).
  • Iterative Correction: Re-align segments after detecting drift to minimize error propagation.

Why Juniors Miss It

  • Overreliance on Global Alignment: Juniors often use SequenceMatcher without considering local phonetic variations.
  • Ignoring Domain-Specific Rules: Lack of awareness about Quranic phonetic rules (e.g., Wasl) leads to suboptimal solutions.
  • Failure to Handle Long Sequences: Insufficient testing on long Ayahs masks the drift issue.

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