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This work introduces a maximum-likelihood based model order (MO) selection technique for spectral envelopes to apply speaker dependent adaptation in the feature-space similar to vocal tract length normalization. Speech recognition systems based on spectral envelopes are using a fixed MO for the underlying linear parametric model. Using a fixed MO over different speakers or channels might not be optimal. To address this problem we investigated the use of warped and scaled minimum variance distortionless response spectral estimation techniques with speaker dependent MOs based on a maximum-likelihood criteria. Comparing experimental results on the Translanguage English Database we can show an improvement by 1,9% relative compared to the word error rate by the fixed MO and 3,5% relative to the traditional Mel-frequency cepstral coefficients.