Does feature normalization improve performance of Hidden Markov Models?

For training a Hidden Markov Model (HMM) on a multivariate, continuous time series, is it preferable to scale the data somehow? Some pre-processing steps may be:

1. Normalize to 0-mean and unit-variance
2. Scale to [-1, 1] interval
3. Scale to [0, 1] interval

With neural networks, the rationale behind scaling is to get an "un-squished" error surface that is easier to navigate in.

HMMs use the Baum-Welch algorithm, which is a variation on the Expectation Maximization (EM) algorithm, to learn parameters.

Is EM sensitive to scale of features? Is there some motivation for normalization for HMMs?