SNR calculation confusion
As Jason R has already mentioned in his comment, the noise reduction algorithm obviously introduces some amplification which renders the SNR estimates we proposed in our earlier answers useless. This becomes clear if we look at how the SNR is calculated. 

Let $x_k$ be the speech signal without noise and let $n_k$ be the noise. Then the SNR before noise reduction is
$$
\gamma_{before} = \frac{\sum x_k^2}{\sum n_k^2}
$$

Now the noise reduction (NR) is applied to both signals yielding the pure speech signal after NR $\tilde{y}_k \neq x_k$.  Applying NR to the noisy signal produces some new signal $y_k + \tilde{n}_k$. As can be seen from your screenshots $y_k \neq \tilde{y}_k$. If we apply dspGurus method for calculating the SNR after NR we obtain
$$
\gamma_{after} = \frac{\sum \tilde{y}_k}{\sum [y_k - \tilde{y}_k + \tilde n_k]^2}
$$
The denominator not only conatins the residual noise but also an additional error $y_k - \tilde{y}_k$. This leads to a wrong estimation and a much lower SNR value.

To overcome this problem, you could try to normalize the signals. This will be difficult, because obiously the NR algorithm scales the noisy and the noise free signal differently. If you've implemented the algorithm yourself, you should try to fix this.

Another possible solution is to estimate the noise power in a region where the original signal contained silence and to calculate the total signal power in a region that contains noise and signal:


Take a region from the noisy signal after NR that only contains noise and calculate the mean power denoted as $\sigma_{n}^2$
Take a region from the noisy signal after NR that contains noise and speech and calculate the mean power denoted as $\sigma_{n,s}^2$


Now the SNR after NR can be estimated as
$$
\gamma_{after} = \frac{\sigma_{n,s}^2 - \sigma_{n}^2}{\sigma_{n}^2}
$$

How good this estimation is, depends on how good the following prerequisites are fulfilled in your case:


A large number of samples should be used to calculate the mean powers. You used 1401, that's not much.
Noise and signal after NR have to be uncorrelated. The NR algorithm possibly introduces noise that is a function of the signal. So this condition might not be fulfilled.
The mean noise power should be constant over time. I think this is fulfilled.