-features a performance of $85\%$ can be achieved. When applying smoothing this
-can be increased until\todo{results}.
+features a performance of $85\%$ can be achieved which is similar to the same
+techniques on regular singing. This means that it might be suitable as a
+pre-processing step for lyrics forced alignment.
+
+Future interesting research includes doing the actual forced alignment. This
+probably requires entirely different models. The models used for real speech
+are probably not suitable because the acoustic properties of a regular singing
+voice is very different from a growling voice, let alone speech.
+
+Secondly, it would be interesting if a model could be trained that could
+discriminate a singing voice for all styles of singing including growling.
+Moreover, it is possible to investigate the performance of detecting growling
+on regular singing-voice trained models and the other way around.