The post is about limited-vocabulary speech recognition, but it still may be interesting for the DeepSpeech project.
The most important conclusion was that, without changing the model or test data at all, the top-one accuracy increased by over 4%, from 85.4% to 89.7%. This was a dramatic improvement, and was reflected in much higher satisfaction when people used the model in the Android or Raspberry Pi demo applications. I’m confident I would have achieved a much lower improvement if I’d spent the time on model adjustments, even though I’m currently using an architecture that I know is behind the state of the art.