Update README.md

README.md

@@ -19,7 +19,7 @@ metrics:
 
 This repository provides all the necessary tools to perform automatic speech
 recognition from an end-to-end system pretrained on LibriSpeech (EN) within
-SpeechBrain. For a better experience we encourage you to learn more about
+SpeechBrain. For a better experience, we encourage you to learn more about
 [SpeechBrain](https://speechbrain.github.io). The given ASR model performance are:
 
 | Release | Test clean WER | Test other WER | GPUs |
@@ -28,21 +28,21 @@ SpeechBrain. For a better experience we encourage you to learn more about
 
 ## Pipeline description
 
-This ASR system is composed with 3 different but linked blocks:
+This ASR system is composed of 3 different but linked blocks:
 1. Tokenizer (unigram) that transforms words into subword units and trained with
 the train transcriptions of LibriSpeech.
 2. Neural language model (Transformer LM) trained on the full 10M words dataset.
 3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
-N blocks of convolutional neural networks with normalisation and pooling on the
+N blocks of convolutional neural networks with normalization and pooling on the
 frequency domain. Then, a bidirectional LSTM with projection layers is connected
 to a final DNN to obtain the final acoustic representation that is given to
 the CTC and attention decoders.
 
 ## Intended uses & limitations
 
-This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model
-for the english language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks
-detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is
+This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model
+for the English language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks
+detailed above can be extracted and connected to your custom pipeline as long as SpeechBrain is
 installed.
 
 ## Install SpeechBrain