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Recently an unsupervised learning scheme for Hidden Markov Models (HMMs) used in acoustical Language Identification (LID) based on Parallel Phoneme Recognizers (PPR) was proposed. This avoids the high costs for orthographically transcribed speech data and phonetic lexica but was found to introduce a considerable increase of classification errors. Also very recently discriminative Minimum Language Identification Error (MLIDE) optimization of HMMs for PPR based LID was introduced that again only requires language tagged speech data and an initial HMM. The described work shows how to combine both approaches to an unsupervised and discriminative learning scheme. Experimental results on large telephone speech databases show that using MLIDE the relative increase in error rate introduced by unsupervised learning can be reduced from 61% to 26%. The absolute difference in LID error rate due to the supervised learning step is reduced from 4.1% to 0.8%.