Abstract: Systems and methods for adjusting a discrete acoustic model complexity in an automatic speech recognition system. In some cases, the systems and methods include a discrete acoustic model, a pronunciation dictionary, and optionally a language model or a grammar model. In some cases, the methods include providing a speech database comprising multiple pairs, each pair including a speech recording called a waveform and an orthographic transcription of the waveform; constructing the discrete acoustic model by converting the orthographic transcription into a phonetic transcription; parameterizing the speech database by transforming the waveforms into a sequence of feature vectors and normalizing the sequences of the feature vectors; and training the acoustic model with the normalized sequences of the feature vectors, wherein the complexity PI of the discrete acoustic model is further adjusted through a procedure that uses a given generalization coefficient N. Other implementations are described.