The high error rate in spontaneous speech recognition is due in part to the poor modeling of pronunciation variations. An analysis of acoustic data reveals that pronunciation variations include both complete changes and partial changes. Complete changes are the replacement of a canonical phoneme by another alternative phone, such as ‘b’ being pronounced as ‘p’. Partial changes are the variations within the phoneme, such as nasalization, centralization, voiceless, voiced, etc. Most current work in pronunciation modeling attempts to represent pronunciation variations either by alternative phonetic representations or by the concatenation of subphone units at the hidden Markov state level. In this paper, we show that partial changes are a lot less clear-cut than previously assumed and cannot be modeled by mere representation by alternate phones or a concatenation of phone units. We propose modeling partial changes through acoustic model reconstruction. We first propose a partial change phone model (PCPM) to differentiate pronunciation variations. In order to improve the model resolution without increasing the parameter size too much, PCPM is used as a hidden model and merged into the pre-trained acoustic model through model reconstruction. To avoid model confusion, auxiliary decision trees are established for PCPM triphones, and one auxiliary decision tree can only be used by one standard decision tree. The acoustic model reconstruction on triphones is equivalent to decision tree merging. The effectiveness of this approach is evaluated on the 1997 Hub4NE Mandarin Broadcast News corpus (1997 MBN) with different styles of speech. It gives a significant 2.39% syllable error rate absolute reduction in spontaneous speech.