My team is building a speech recognition model. As far as I know, Vocal Tract Length Normalization (VTLN) is an effective warping method that may help improve the model performance by diminishing spectral variance.
However, most implementations I've seen so far seem to use maximum-likelihood to estimate the warping factor, which could be slow when the model is used to recognize real-time audio streams (we plan to deploy the model on hardware so some computations in calculating probability such as division may be expensive).
Is there a fast implementation of VTLN? It's okay if the performance is slightly worse than the ML implementation.