Since $x \in conv(S)$ then there are nonnegative numbers $\lambda_1,\lambda_2, ... \lambda_t$ and vectors $x_1,x_2,...,x_t \in S$ such that $\sum_j \lambda_j=1$ and $x=\sum_j\lambda_jx_j$.
Suppose $x_1,x_2,...,x_t $ are not affine independent. Then there are numbers $\mu_1 ... \mu_t$ not all zeros such that $\sum_{j=1}^t\mu_j x_j=0$ and $\sum_{j=1}^t\mu_j =0$.
Since the $\mu_j$s are not all zero and sum to zero, at least one of these numbers must be positive.
Let $\mu_1>0$. We now multiply the equation $\sum_{j=1}^t\mu_j x_j=0$ by a nonnegative number $\alpha$ and substract with $x=\sum_j\lambda_jx_j$. This gives:
$$
x=\sum_j(\lambda_j-\alpha\mu_j)x_j
$$
Note that $x=\sum_j(\lambda_j-\alpha\mu_j) = \sum_j\lambda_j-\alpha\sum_j\mu_j=1$.
When $\alpha=0$ this is just the original representation of x.
Now gradually increase $\alpha $ from zero to $\alpha_0$ until one of the coefficients $\lambda_j-\alpha\mu_j$ becomes zero. This means that we have found a new reprenstation of $x$ as a convex combination of $t-1$ vectors from S.
Continue this reduction process until we have $x$ written as a convex combination of at most $n+1$ affinely independent points.
*I got this from my lecture notes, all credit goes to my professor.