Update README.md

README.md

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 A dataset containing English speech with grammatical errors, along with the corresponding transcriptions. Utterances are synthesized using a 
 text-to-speech model, whereas the grammatically incorrect texts come from the [C4_200M](https://aclanthology.org/2021.bea-1.4) synthetic dataset.
+
+## Introduction
+
+The Synthesized English Speech with Grammatical Errors (SESGE) dataset was developed to support the [DeMINT](https://github.com/transducens/demint) project.
+The objective of DeMINT is to develop an intelligent tutoring system that helps non-native English speakers improve their language skills by analyzing and providing 
+feedback on the transcripts of their online meetings. As part of this, a system able to transcribe spoken English keeping the original 
+grammatical errors intact was essential. 
+Existing speech-to-text (STT) models like Whisper tend to correct grammatical errors due to their strong internal language models, making them unsuitable for this task. 
+Therefore, SESGE was created to train a custom STT model that could accurately transcribe spoken English with grammatical errors preserved.
+
+## Dataset Creation
+
+Given the absence of a suitable dataset for training an error-preserving STT system, DeMINT fine-tuned a Whisper model with data from two primary sources: 
+
+- [COREFL](https://www.peterlang.com/document/1049094)
+The COREFL dataset consists of essays written by non-native English students with various levels of proficiency. 
+While some of these essays have associated audio recordings, the majority do not. 
+To expand the audio dataset, we used the [StyleTTS2](https://arxiv.org/abs/2306.07691) text-to-speech model to generate synthetic audio for the remaining texts. 
+Multiple voices were used for synthesis to increase the diversity of the dataset. 
+COREFL also includes audio directly recorded by students, which introduces natural speech variability and common errors found among L1-Spanish speakers, 
+a key demographic for the DeMINT project.
+
+- [C4_200M](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction)
+The C4_200M dataset contains synthetically generated English sentences with grammatical errors, produced using a corruption model. 
+Like with COREFL, StyleTTS2 was employed to synthesize audio from these texts, diversifying the voices to enhance the training set. 
+This dataset primarily provides varied sentence structures and error types, although with a limited number of distinct voices.
+
+Due to licensing restrictions associated with the COREFL dataset, only the portion derived from the C4_200M dataset is publicly available as part of the 
+SESGE dataset. This means that while COREFL data was used during our training, only the C4_200M-based data is included in this dataset.
+
+Training samples comprise **28,592** utterances from C4_200M.
+
+## Models
+
+Two models were trained on the SESGE dataset by fine-tuning Whisper, enabling error-preserving STT. These models are available on the Hugging Face Hub:
+
+- [Error-Preserving Whisper Model](https://huggingface.co/Transducens/error-preserving-whisper)
+- [Error-Preserving Whisper Distilled Model](https://huggingface.co/Transducens/error-preserving-whisper-distilled)
+
+Both models have been optimized to transcribe spoken English while retaining grammatical errors, making them suitable for language-learning applications 
+where fidelity to spoken errors is essential.
+
+## How to Cite
+
+If you use the SESGE dataset, please cite the following paper:
+
+```bibtex
+@inproceedings{demint2024,
+  author = {Pérez-Ortiz, Juan Antonio and 
+    Esplà-Gomis, Miquel and 
+    Sánchez-Cartagena, Víctor M. and 
+    Sánchez-Martínez, Felipe and 
+    Chernysh, Roman and 
+    Mora-Rodríguez, Gabriel and 
+    Berezhnoy, Lev},
+  title = {{DeMINT}: Automated Language Debriefing for English Learners via {AI} 
+           Chatbot Analysis of Meeting Transcripts},
+  booktitle = {Proceedings of the 13th Workshop on NLP for Computer Assisted Language Learning},
+  month = october,
+  year = {2024},
+  url = {https://aclanthology.org/volumes/2024.nlp4call-1/},
+}
+```