process Chris' comments

[asr1617.git] / conclusion.tex

diff --git a/conclusion.tex b/conclusion.tex
index 778b4fc..3adbc05 100644 (file)
--- a/conclusion.tex
+++ b/conclusion.tex
@@ -28,8 +28,8 @@ Therefore the resulting model can be very general. On the other side, it could
 also result in a model that is overfitted the three islands in entire space of
 grunting voices.
 
-In this case it seems that the model generalizes well. The alien data similar
-to the trainingsdata offered to the model results in a good performance.
+In this case it seems that the model generalizes well. The alien data --- similar
+to the training data --- offered to the model, results in a good performance.
 However, alien data that has a very different style does not perform as good.
 While testing \emph{Catacombs} the performance was very poor. Adding
 \emph{Catacombs} or a similar style to the training set can probably overcome
@@ -64,8 +64,8 @@ converting the waveforms to \gls{MFCC} can be performed by the neural network.
 The current decorrelation step might be inefficient or unnatural. The \gls{ANN}
 train the weights in such a way that performance is maximized. It would be
 interesting to see whether this results in a different normalization step. The
-downside of this is that training the model is complexer because there are many
-more weights to train.
+downside of this is that training the model is more complex because there are
+many more weights to train.
 
 \paragraph{Genre detection: }
 \emph{Singing}-voice detection and \emph{singer}-voice can be seen as a crude