Marginal Covariance of State Vector in a Linear Gaussian State Space Model Paper: A Unifying Review of Linear Gaussian Models by Roweis & Ghahramani
The generative model is the typical state space model written as
\begin{align}
\text{state transition equation: }{\bf x}_t &= {\bf A} {\bf x}_{t-1} + {\bf w}_t && {\bf w}_t \sim \mathcal{N} \left( {\bf 0}, {\bf Q} \right) \\
\text{observation equation: }{\bf y}_t &= {\bf C} {\bf x}_t + {\bf v}_t && {\bf v}_t \sim \mathcal{N} \left( {\bf 0}, {\bf R} \right)
\end{align}
where ${\bf A}$ is the $k \times k$ state transition matrix and ${\bf C}$ is the $p \times k$ observation matrix.
In the paper on. page 2, the authors write

Notice that there is degeneracy in the model: all of the structure in the matrix $\bf Q$ can be moved into the matrices $\bf A$ and $\bf C$. This means we can without loss generality work with models in which $\bf Q$ is the identity matrix.

There is a footnote associated with the passage and it reads

In particular, since is it a covariance matrix, $\bf Q$ is symmetric positive semi-definite and thus can be diagonalized to the form $\bf E \Lambda E^{\top}$ (where $\bf E$ is the rotation matrix of eigenvectors and ${\bf \Lambda}$ is a diagonal matrix of eigenvalues). Thus for any model in which $\bf Q$ is not the identity matrix, we can generate an exactly equivalent model using a new state vector ${\bf x}' = {\bf \Lambda}^{-1/2} {\bf E}^{\top} {\bf x}$ with ${\bf A}' = ( {\bf \Lambda}^{-1/2} {\bf E}^{\top} ) \, {\bf A} \, ( {\bf E} {\bf \Lambda}^{1/2} )$ and ${\bf C}' = {\bf C} ( {\bf E} {\bf \Lambda}^{1/2})$ such that the new covariance of ${\bf x}'$ is the identity matrix: ${\bf Q}' = {\bf I}$.

Question: How is the new covariance of $\bf x'$ equal to the identity matrix?
\begin{align}
\text{Var} ({\bf x}') = {\bf \Lambda}^{-1/2} {\bf E}^{\top} \text{Var} ({\bf x}) {\bf E} {\bf \Lambda}^{-1/2}
\end{align}
equals the identity matrix if $\text{Var} ({\bf x})$ is the identity matrix. What in the model tells use that this is the case?

Update Mar 27: I believe the authors are referring to the conditional covariance in the footnote.
From the generative model we can see that the conditional distribution of ${\bf x}_t \, \vert \, {\bf x}_{t-1}$ is Gaussian with conditional mean ${\bf A} {\bf x}_{t-1}$ and conditional covariance ${\bf Q}$.
Using the transformed state vector, the new generative model is now
\begin{align}
\text{new state transition equation: }{\bf x}'_t &= {\bf A}' {\bf x}'_{t-1} + {\bf w}'_t && {\bf w}'_t \sim \mathcal{N} \left( {\bf 0}, {\bf I} \right) \\
\text{new observation equation: }{\bf y}_t &= {\bf C}' {\bf x}'_t + {\bf v}_t && {\bf v}_t \sim \mathcal{N} \left( {\bf 0}, {\bf R} \right).
\end{align}
Thus, the conditional distribution of ${\bf x}'_t \, \vert \, {\bf x}'_{t-1}$ is Gaussian with conditional mean ${\bf A}' {\bf x}'_{t-1}$ and conditional covariance ${\bf I}$.
 A: I think you have it backwards. $\bf Q$, not $\bf I$ is the variance of $\bf x$.
The intended result can become clearer from some quick matrix multiplications to rearrange your equation.
$$
\begin{align}
\text{Var} ({\bf x}') &= {\bf \Lambda}^{-1/2} {\bf E}^{\top} \text{Var} ({\bf x}) {\bf E} {\bf \Lambda}^{-1/2} \\
{\bf \Lambda}^{1/2} \text{Var} ({\bf x}') &= {\bf E}^{\top} \text{Var} ({\bf x}) {\bf E} {\bf \Lambda}^{-1/2} \\
{\bf E}{\bf \Lambda}^{1/2} \text{Var} ({\bf x}') &= \text{Var} ({\bf x}) {\bf E} {\bf \Lambda}^{-1/2} \\
&\vdots \\
{\bf E}{\bf \Lambda}^{1/2} \text{Var} ({\bf x}') {\bf \Lambda}^{1/2} {\bf E}^{\top} &= \text{Var} ({\bf x}) \\
\end{align}
$$
We know from the given noise process that $\text{Var} ({\bf x}) = {\bf Q}$. Now if we wager that $\text{Var} ({\bf x'}) = {\bf I}$, we see that the left-hand side of the equation becomes our eigendecomposition of ${\bf Q}$, so the two sides are equal. That is, the variance of $\bf x'$ is $\bf I$ when the variance of $\bf x$ is $\bf Q$.
Alternatively, you could go the direct route.
$$
\begin{align}
\text{Var} ({\bf x}') &= {\bf \Lambda}^{-1/2} {\bf E}^{\top} \text{Var} ({\bf x}) {\bf E} {\bf \Lambda}^{-1/2} \\
\text{Var} ({\bf x}') &= {\bf \Lambda}^{-1/2} {\bf E}^{\top} {\bf Q} {\bf E} {\bf \Lambda}^{-1/2} \\
\text{Var} ({\bf x}') &= {\bf \Lambda}^{-1/2} {\bf E}^{\top} {\bf E} {\bf \Lambda} {\bf E}^{\top} {\bf E} {\bf \Lambda}^{-1/2} \\
\text{Var} ({\bf x}') &= {\bf \Lambda}^{-1/2} {\bf \Lambda}  {\bf \Lambda}^{-1/2} \\
\text{Var} ({\bf x}') &= {\bf I} \\
\end{align}
$$
