Phonetic normalization plays a crucial role in speech recognition and analysis, ensuring the comparability of features derived from raw audio data. However, in the current paradigm of fine-tuning pre-trained large transformer models, phonetic normalization is not deemed a necessary step; instead, it is implicitly executed within the models. This study investigates the normalization process within transformer models, especially wav2vec 2.0. Through a comprehensive analysis of embeddings from models fine-tuned for various tasks, our results demonstrate that fine-tuning wav2vec 2.0 effectively achieves phonetic normalization by selectively suppressing task-irrelevant information. We found that models fine-tuned for multiple tasks retain information for both tasks without compromising performance, and that suppressing task-irrelevant information is not necessary for effective classification. These findings provide new insights into how phonetic normalization can be flexibly achieved in speech models and how it is realized in human speech perception.