The previous lessons identified the problems of speech recognition, and provided a traditional ASR solution using feature extraction HMMs and language models. These systems have gotten better and better since they were introduced in the 1980’s. But is there a better way? As computers become more powerful and data more available, deep neural networks have become the go to solution for all kinds of large probabilistic problems including speech recognition. In particular, recurrent neural networks RNNs can be leveraged, because these types of networks have temporal memory, an important characteristic for training and decoding speech. This is a hot topic and an area of active research. The information that follows is primarily based on recent research presentations. The tech is bleeding edge, and changing rapidly but we’re going to jump right in. Here we go.