done

README.md

@@ -10,7 +10,9 @@ This repository contains a number of experiments for the [PSST Challenge](https:
 
 As the test set is unavailable, all numbers are based on the validation set.
 
-The overall best performing model was based on 
+The experiments in the tables below were finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec)
+
+Our overall best performing model (**FER** 9\.2%, **PER:** 21\.0%) was based on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) (git tag: `larger-rir`), with the TIMIT subset augmented with Room Impulse Response, based on the experiments below, on the base model. 
 
 ## Augmented TIMIT subset
 
@@ -44,7 +46,7 @@ We experimented with a number of language model configurations, combining the da
 
 We tried combining CMUdict data in a number of ways: unmodified, with a silence token added at the start of the pronunciation, at the end, and at both the start and the end.
 
-The best result was from a 5-gram model, with silences added at the end of the CMUdict data.
+The best result was from a 5-gram model, with silences added at the end of the CMUdict data (git tag: `lm-nosil-cmudict-sile.5`).
 
 Evaluation was performed using scripts provided by the PSST Challenge's organisers, so there are no scripts in place to automatically use the LM with the transformers library.