Update README.md

README.md

@@ -53,3 +53,109 @@ configs:
   - split: test
     path: data/test-*
 ---
+# 🗣️ LibriConvo-Segmented
+
+**LibriConvo-Segmented** is a segmented version of the **LibriConvo** corpus — a **simulated multi-speaker conversational dataset** built using *Speaker-Aware Conversation Simulation (SASC)*.  
+It is designed for **training and evaluation of multi-speaker speech processing systems**, including **speaker diarization**, **automatic speech recognition (ASR)**, and **overlapping speech modeling**.
+
+This segmented version provides ≤30-second conversational fragments derived from full LibriConvo dialogues, with 40% of them having room impulse responses applied on them. All conversations have 2 speakers.
+
+The full paper, detailing the creation of the corpus, as well as baseline ASR and diarization results can be found here: https://arxiv.org/abs/2510.23320
+
+---
+
+## 🧠 Overview
+
+**LibriConvo** ensures **natural conversational flow** and **contextual coherence** by:
+
+- Organizing LibriTTS utterances by **book** to maintain narrative continuity.
+- Using statistics from **CallHome** for pause modeling.  
+- Applying **compression** to remove excessively long silences while preserving turn dynamics.  
+- Enhancing **acoustic realism** via a novel **Room Impulse Response (RIR) selection procedure**, ranking configurations by spatial plausibility.  
+- Producing **speaker-disjoint splits** for robust evaluation and generalization.
+
+In total, the full LibriConvo corpus comprises **240.1 hours** across **1,496 dialogues** with **830 unique speakers**.  
+This segmented release provides **shorter, self-contained audio clips** suitable for fine-tuning ASR and diarization models.
+
+---
+
+## 📦 Dataset Summary
+
+| Split | # Examples |
+|:------|------------:|
+| Train | 138,683     | 
+| Validation | 15,893 | 
+| Test | 16,030 |
+
+**Total size:** ≈ 145 GB  
+**Sampling rate:** 16 kHz  
+**Audio format:** WAV (mono)  
+**Split criterion:** Speaker-disjoint  
+
+---
+
+## 📂 Data Structure
+
+Each row represents a single speech segment belonging to a simulated conversation between two speakers.
+
+| Field | Type | Description |
+|:------|:----:|:------------|
+| `conversation_id` | string | Conversation identifier |
+| `utterance_idx` | int64 | Utterance index within the conversation |
+| `abstract_symbol` | string | High-level symbolic utterance ID ('A' or 'B') |
+| `transcript` | string | Text transcription of the utterance |
+| `duration_sec` | float64 | Segment duration (seconds) |
+| `rir_file` | string | Room impulse response file used |
+| `delay_sec` | float64 | Delay applied for realistic speaker overlap |
+| `start_time_sec`, `end_time_sec` | float64 | Start and end times within the conversation |
+| `abs_start_time_sec`, `abs_end_time_sec` | float64 | Global (absolute) start and end times |
+| `segment_id` | int64 | Local segment index |
+| `segment_conversation_id` | string | Unique segment identifier |
+| `split` | string | One of `train`, `validation`, or `test` |
+| `audio` | Audio (16 kHz) | Decoded audio data |
+---
+
+## 🚀 Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("gedeonmate/LibriConvo-segmented")
+
+print(ds)
+# DatasetDict({
+#     train: Dataset(...),
+#     validation: Dataset(...),
+#     test: Dataset(...)
+# })
+```
+
+---
+
+📚 Citation
+
+If you use the LibriConvo dataset or the associated Speaker-Aware Conversation Simulation (SASC) methodology in your research, please cite the following papers:
+
+```
+@misc{gedeon2025libriconvo,
+  title         = {LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization},
+  author        = {Máté Gedeon and Péter Mihajlik},
+  year          = {2025},
+  eprint        = {2510.23320},
+  archivePrefix = {arXiv},
+  primaryClass  = {eess.AS},
+  url           = {https://arxiv.org/abs/2510.23320}
+}
+```
+
+```
+@misc{gedeon2025sasc,
+      title={From Independence to Interaction: Speaker-Aware Simulation of Multi-Speaker Conversational Timing}, 
+      author={Máté Gedeon and Péter Mihajlik},
+      year={2025},
+      eprint={2509.15808},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2509.15808}, 
+}
+```