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Automatic forced alignment is standard in phonetic research, but the minimum data needed for reliable acoustic measurements remains unclear. This study examines how token quantity affects alignment reliability using the MFA on the TIMIT corpus, with manual phoneme boundaries as the gold standard. Focusing on vowels and three acoustic features (Duration, F1, F2) token subsets were incrementally sampled and mixed-effects models fitted at each step to evaluate automatic versus manual measurements. Results show 85% of vowel-feature combinations improve significantly with increasing tokens, with F1 showing the most consistent gains. Most vowels stabilise around 50% of available tokens, though variability exists: Duration stabilises earliest (≈730 tokens) while F2 requires the most data (≈2,335 tokens). These findings offer empirically grounded guidelines for minimum token requirements, with practical implications for large-scale sociophonetic studies and low-resource language research.