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+Automatic Speech Recognition (ASR)
+==================================
+
+ASR, or Automatic Speech Recognition, refers to the problem of getting a program to automatically transcribe spoken language 
+(speech-to-text). Our goal is usually to have a model that minimizes the Word Error Rate (WER) metric when transcribing speech input. 
+In other words, given some audio file (e.g. a WAV file) containing speech, how do we transform this into the corresponding text with 
+as few errors as possible?
+
+Traditional speech recognition takes a generative approach, modeling the full pipeline of how speech sounds are produced in order to 
+evaluate a speech sample. We would start from a language model that encapsulates the most likely orderings of words that are generated 
+(e.g. an n-gram model), to a pronunciation model for each word in that ordering (e.g. a pronunciation table), to an acoustic model that 
+translates those pronunciations to audio waveforms (e.g. a Gaussian Mixture Model).
+
+Then, if we receive some spoken input, our goal would be to find the most likely sequence of text that would result in the given audio 
+according to our generative pipeline of models. Overall, with traditional speech recognition, we try to model ``Pr(audio|transcript)*Pr(transcript)``, 
+and take the argmax of this over possible transcripts.
+
+Over time, neural nets advanced to the point where each component of the traditional speech recognition model could be replaced by a 
+neural model that had better performance and that had a greater potential for generalization. For example, we could replace an n-gram 
+model with a neural language model, and replace a pronunciation table with a neural pronunciation model, and so on. However, each of 
+these neural models need to be trained individually on different tasks, and errors in any model in the pipeline could throw off the 
+whole prediction.
+
+Thus, we can see the appeal of end-to-end ASR architectures: discriminative models that simply take an audio input and give a textual 
+output, and in which all components of the architecture are trained together towards the same goal. The model's encoder would be 
+akin to an acoustic model for extracting speech features, which can then be directly piped to a decoder which outputs text. If desired, 
+we could integrate a language model that would improve our predictions, as well.
+
+And the entire end-to-end ASR model can be trained at once--a much easier pipeline to handle!
+
+The full documentation tree is as follows:
+
+.. toctree::
+   :maxdepth: 8
+
+   models
+   datasets
+   asr_language_modeling
+   results
+   configs
+   api
+
+Resources and Documentation
+---------------------------
+
+Hands-on speech recognition tutorial notebooks can be found under `the ASR tutorials folder <https://github.com/NVIDIA/NeMo/tree/v1.0.2/tutorials/asr/>`_.
+If you are a beginner to NeMo, consider trying out the `ASR with NeMo <https://github.com/NVIDIA/NeMo/tree/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb>`_ tutorial.
+This and most other tutorials can be run on Google Colab by specifying the link to the notebooks' GitHub pages on Colab.
+
+If you are looking for information about a particular ASR model, or would like to find out more about the model
+architectures available in the `nemo_asr` collection, refer to the :doc:`Models <./models>` section.
+
+NeMo includes preprocessing scripts for several common ASR datasets. The :doc:`Datasets <./datasets>` section contains instructions on 
+running those scripts. It also includes guidance for creating your own NeMo-compatible dataset, if you have your own data.
+
+Information about how to load model checkpoints (either local files or pretrained ones from NGC), as well as a list of the checkpoints 
+available on NGC are located on the :doc:`Checkpoints <./results>` section.
+
+Documentation regarding the configuration files specific to the ``nemo_asr`` models can be found on the :doc:`Configuration Files <./configs>` section.