# Speech Recognition - [Recurrent Neural Network Based Language Model](https://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf) - 50% reduction of [Perplexity](Perplexity.md) - mixture of several [Basic RNN Architectures](Basic%20RNN%20Architectures.md) - [Wall Street Journal task](Wall%20Street%20Journal%20task.md) - connectionist language models are superior to standard [n gram](n%20gram) techniques, except their high computational (training) complexity - break the myth that language modeling is just about counting n-grams, and that the only reasonable way how to improve results is by acquiring new training dat - [Towards End-To-End Speech Recognition with Recurrent Neural Networks](http://proceedings.mlr.press/v32/graves14.pdf) - character-level speech recognition system that directly transcribes audio data with text using a recurrent neural network - combination of the deep bidirectional LSTM recurrent neural network architecture and a modified Connectionist Temporal Classification ([CTC](CTC.md)) objective function - word error rate - [Wall Street Journal task](Wall%20Street%20Journal%20task.md)