Updated README.md to provide more insight on BLEU and specific appendices
diff --git a/README.md b/README.md
index 64e2d84c..2916e91c 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ There are five model sizes, four with English-only versions, offering speed and
 
 The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
 
-Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356). The smaller, the better.
+Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model (The smaller the numbers, the better the performance). Additional WER scores corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4. Meanwhile, more BLEU (Bilingual Evaluation Understudy) scores can be found in Appendix D.3. Both are found in [the paper](https://arxiv.org/abs/2212.04356). 
 
 ![WER breakdown by language](https://raw.githubusercontent.com/openai/whisper/main/language-breakdown.svg)
 

**Improves Documentation**

Previous documentation did not include a description of what BLEU acronym stood for. Additionally, did not provide the specific appendices to find additional comparisons for WER and BLEU scores for other models.

This change improves readability of the Available models and languages section of the README.md and includes the full description of the acronym BLEU and specific appendices within the research paper.