This YouTube video demonstrates building a real-time speech recognition system using deep learning. The creator details the challenges of speech recognition (acoustic and linguistic variations), explains the use of an acoustic model (RNN) trained on Common Voice dataset and a language model (KenLM) with a rescoring algorithm (CTC beam search) to improve accuracy. A small, fast model is prioritized, resulting in a system that performs well with the creator's voice but poorly with others due to data bias. The creator provides code and a pre-trained model for viewers to fine-tune with their own data. So because there's so many variations and nuances in the physical properties of speech, it makes it extremely hard to come up with all the rules possible for speech recognition. Not only do you have to deal with the physical properties of speech, but you have to deal with the linguistic properties of it as well. You can naively take the highest probable word of each time step and emit that as your transcript, but your network can easily make linguistic mistakes like using the word red for color when it should be read for reading.