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In a previous work, we introduced the use of log-likelihood ratios of phone posterior probabilities, called Phone Log-Likelihood Ratios (PLLR) as features for language recognition under an iVector-based approach, yielding high performance and promising results. However, the high dimensionality of the PLLR feature vectors (with regard to MFCC/SDC features) results in comparatively higher computational costs. In this work, several supervised and unsupervised dimensionality reduction techniques are studied, based on either fusions or selection of phone posteriors, finding that PLLR feature vectors can be reduced to almost a third of their original size attaining similar performance. Finally, Principal Component Analysis (PCA) is also applied to the original PLLR vector as a feature projection method for comparison purposes. Results show that PCA stands out among all the techniques studied, revealing that it does not only reduce computational costs, but also improves system performance significantly.