End-to-end deep learning systems for automatic speech recognition (ASR) have been around for a while now since Deep Speech (2014), but I noticed that DNN-HMM based methods are still performing well and making it to the charts like here.
Does that mean it is still not settled which system is better? Or do they win based on conditions? Who is better when you just have speech training data in the order of hundreds of hours and not tens of thousands? Which system is better in real life and not on super clean data?