Gaussian distribution with condition? What does this expression mean?
Normal distribution with condition
I am reading a research paper and found the following expression (Eq.28 in the paper below).

It means a Gaussian distribution, but the mean component seems conditional probability-like expression $\it{\bf{s}}_t | \it{\bf{m}}_{b, t, m}^{(j)}$.
I have never seen this expression before and cannot find any info about it.
The variables $\it{\bf{s}}_t$ and $\it{\bf{m}}_{b, t, m}^{(j)}$ are both vectors and $\bf{\Sigma}_{b}$ is a covariance matrix.
Does anybody have an idea of what this expression means?
Original paper where the expression is.
The original paper can be found here: https://eprints.soton.ac.uk/437941/1/08340823.pdf
Thanks in advance.
$$\mathcal{N}(x|m,\Sigma)=(2\pi)^{-k/2}|\Sigma|^{-1/2}\exp\left(-2^{-1}(x-m)^T\Sigma^{-1}(x-m)\right)$$
where $x,m$ are vectors of dimension $k$, $\Sigma$ is a $k\times k$ matrix, $|\Sigma|$ is its determinant. The vector $m$ represents the mean of the multivariate normal, and $\Sigma$ the covariance matrix, which must be positive definite.