Update README.md

README.md

@@ -4,58 +4,248 @@ task_categories:
 - automatic-speech-recognition
 language:
 - fo
-pretty_name: FPSC-ASR
+pretty_name: FPSC
 size_categories:
 - 10K<n<100K
 ---
 
-# FPSC-ASR
+# FPSC — Faroese Parliament Speech Corpus
 
-FPSC-ASR is a Faroese parliamentary ASR dataset based on speech recordings from Løgtingið, the Faroese Parliament.
+FPSC is a large-scale Faroese parliamentary speech corpus constructed from publicly available recordings from *Løgtingið*, the Parliament of the Faroe Islands.
 
-The dataset contains WAV audio files, parliamentary metadata, and machine-generated transcripts selected by ROVER-style system voting.
+The dataset contains approximately:
 
-## Format
+- 1,600 hours of speech
+- 89,000+ parliamentary speeches
+- 368 parliamentary meetings
+- 75 unique speakers
+- speaker demographic metadata
+- dialect metadata
+- machine-generated weak transcripts
+- ROVER voting metadata
+- audio aligned at speech level
 
-The dataset follows a standard Hugging Face audio dataset format using `AudioFolder`.
+The corpus was created as part of the paper:
 
-Main ASR columns:
+> **FPSC: A Sustainable Pipeline for Building a Faroese Parliamentary Speech Corpus**  
+> Dávid í Lág, Barbara Scalvini, Carlos Mena, Jón Guðnason  
+> LREC 2026
 
-- `audio_id`: original WAV filename
-- `audio`: audio column created by Hugging Face when loading the dataset
-- `speaker_id`: speaker identifier, mapped from `mp_id`
-- `gender`: speaker gender metadata
-- `age`: age group, mapped from `age_group`
-- `duration`: audio duration in seconds, mapped from `length`
-- `normalized_text`: machine-generated transcript, mapped from `winner_text`
-- `dialect`: speaker dialect metadata
-- `winner_text`: original winning ROVER transcript
-- `text`: same as `winner_text`, included for ASR training convenience
+The dataset represents the first large-scale corpus of natural spoken Faroese and is intended for:
 
-ROVER voting columns:
+- Automatic Speech Recognition (ASR)
+- Low-resource speech technology
+- Parliamentary speech analysis
+- Sociolinguistic research
+- Dialect research
+- Weakly supervised ASR training
+- Continual pretraining
+- Multilingual transfer learning
 
-- `confidence`: ROVER voting confidence
-- `winner_model_id`: full Hugging Face model id of the winning model
-- `winner_model_short`: short model name of the winning model
-- `costs`: full ROVER costs dictionary stored as a JSON string
-- `cost_wav2vec2_fo_cpt`: flattened cost for Wav2Vec2-FO-CPT
-- `cost_whisper_fo`: flattened cost for Whisper-FO
-- `cost_wav2vec2_fo`: flattened cost for Wav2Vec2-FO
-- `cost_whisper_no_is_fo`: flattened cost for Whisper-NO/IS/FO
+---
+
+# Dataset Structure
+
+The dataset follows the Hugging Face `Audio` dataset format and contains one row per parliamentary speech segment.
+
+Each entry contains:
+
+- segmented WAV audio
+- machine-generated transcript
+- parliamentary metadata
+- speaker metadata
+- dialect metadata
+- ROVER voting information
+- ASR ensemble metadata
+
+---
+
+# Features
+
+## Audio and Speech Fields
+
+| Field | Description |
+|---|---|
+| `audio` | Hugging Face `Audio` object containing the speech segment |
+| `audio_id` | Unique WAV filename for the speech segment |
+| `duration` | Audio duration in seconds |
+| `length` | Original segment length in seconds |
+| `audio_format` | Audio format (WAV) |
+| `sampling_rate` | Audio sample rate (16 kHz mono) |
+
+---
+
+## Transcript Fields
+
+| Field | Description |
+|---|---|
+| `text` | Final machine-generated transcript selected through ROVER voting |
+| `normalized_text` | Normalized version of the transcript for ASR training |
+| `winner_text` | Raw winning transcript from the ROVER voting process |
+| `language` | Spoken language of the segment (Faroese) |
+
+---
+
+## Speaker Metadata
+
+| Field | Description |
+|---|---|
+| `speaker_id` | Internal speaker identifier |
+| `mp_id` | Parliament member identifier |
+| `name` | Speaker name |
+| `gender` | Speaker gender |
+| `age` | Speaker age |
+| `age_group` | Speaker age group |
+| `date_of_birth` | Speaker date of birth |
+| `city` | Speaker home city |
+| `dialect` | Dialect region |
+| `political_party_affiliation` | Political party affiliation |
+| `mp_url` | URL to parliament member profile |
+
+---
+
+## Parliamentary Metadata
+
+| Field | Description |
+|---|---|
+| `id` | Unique speech segment ID |
+| `meeting_id` | Parliamentary meeting identifier |
+| `url` | URL to the original parliamentary meeting |
+| `date` | Meeting date |
+| `time` | Speech start time |
+| `second` | Start offset in seconds within the meeting |
+| `topic` | Parliamentary agenda topic |
+| `contribution_type` | Type of contribution (speech, remark, chair, etc.) |
+| `location` | Recording location |
+
+---
+
+## ROVER Voting Metadata
+
+The final transcripts were generated using an ensemble of four Faroese-adapted ASR systems combined through ROVER voting.
+
+| Field | Description |
+|---|---|
+| `rover_vote_type` | Type of ROVER voting decision |
+| `confidence` | Confidence score assigned by the voting system |
+| `winner_model_id` | Full Hugging Face model ID of the winning ASR model |
+| `winner_model_short` | Short name of the winning ASR model |
+| `costs` | Full ROVER voting cost dictionary stored as JSON |
+| `cost_wav2vec2_fo_cpt` | Voting cost for Wav2Vec2-FO-CPT |
+| `cost_whisper_fo` | Voting cost for Whisper-FO |
+| `cost_wav2vec2_fo` | Voting cost for Wav2Vec2-FO |
+| `cost_whisper_no_is_fo` | Voting cost for Whisper-NO/IS/FO |
+
+---
+
+# ASR Models Used for Weak Transcription
+
+The corpus transcripts were generated using four Faroese-adapted ASR systems:
+
+| Model | Description |
+|---|---|
+| `Wav2Vec2-FO-CPT` | Continually pretrained Wav2Vec2 XLS-R model adapted on Faroese parliamentary speech |
+| `Wav2Vec2-FO` | Faroese fine-tuned Wav2Vec2 XLS-R model |
+| `Whisper-FO` | Whisper Large model fine-tuned on Faroese speech |
+| `Whisper-NO/IS/FO` | Multilingual Whisper model fine-tuned on Norwegian, Icelandic, and Faroese |
+
+The final transcript for each segment was selected using weighted ROVER voting based on ASR model performance.
+
+---
+
+# Transcript Quality Notice
+
+The transcripts in FPSC are automatically generated and should be treated as **weakly supervised labels**, not manually verified gold-standard transcriptions.
 
-All original parliamentary metadata fields are also included.
+Although the corpus was generated using multiple ASR systems and ROVER consensus voting, transcription errors remain present, especially for:
 
-## Transcript note
+- overlapping speech
+- dialect variation
+- named entities
+- interruptions
+- spontaneous parliamentary speech
 
-The transcripts are machine-generated and should be treated as weak labels, not manually verified gold-standard transcripts.
+The dataset is therefore most suitable for:
 
-## Loading
+- weakly supervised ASR training
+- continual pretraining
+- large-scale speech modeling
+- speech representation learning
+- sociolinguistic analysis
+
+---
+
+# Loading the Dataset
 
 ```python
 from datasets import load_dataset, Audio
 
-ds = load_dataset("davidilag/FPSC-ASR")
+ds = load_dataset("davidilag/FPSC")
 ds = ds.cast_column("audio", Audio(sampling_rate=16000))
 
 print(ds)
 print(ds["train"][0])
+```
+
+---
+
+# Citation
+
+If you use FPSC in your research, please cite:
+
+```bibtex
+@inproceedings{iLag2026FPSC,
+  author = {Dávid í Lág and Barbara Scalvini and Carlos Mena and Jón Guðnason},
+  title = {{FPSC}: A Sustainable Pipeline for Building a Faroese Parliamentary Speech Corpus},
+  booktitle = {Proceedings of the Language Resources and Evaluation Conference (LREC 2026)},
+  year = {2026},
+  address = {Palma de Mallorca, Spain},
+  publisher = {European Language Resources Association (ELRA)},
+  institution = {University of the Faroe Islands},
+  keywords = {Faroese, Parliamentary Speech, Automatic Speech Recognition, Weakly-Supervised Transcription, Whisper, Wav2Vec2},
+  abstract = {We present FPSC, a 1,600-hour Faroese parliamentary speech corpus comprising approximately 89,000 speeches enriched with detailed speaker and linguistic metadata. The corpus was constructed using a sustainable ASR-assisted pipeline combining speech segmentation, multiple Faroese-adapted ASR systems, and ROVER-based consensus voting for weakly supervised transcription. FPSC represents the first large-scale corpus of natural spoken Faroese and provides an open resource for future research in automatic speech recognition and low-resource language technology.}
+}
+```
+
+---
+
+# Repository and Scripts
+
+Processing scripts and pipeline implementation:
+
+- GitHub repository: https://github.com/davidilag/LREC2026
+
+---
+
+# Original Data Source
+
+The original parliamentary recordings and metadata were collected from the official website of *Løgtingið*, the Parliament of the Faroe Islands:
+
+https://www.logting.fo/mal/yvirlit/gerdabokur/
+
+The website provides publicly accessible:
+
+- parliamentary meeting recordings
+- agendas and meeting protocols
+- speaker order and timestamps
+- meeting dates and metadata
+- parliament member information
+
+FPSC was constructed by automatically downloading, processing, segmenting, and transcribing these publicly available parliamentary sessions into a structured speech corpus suitable for ASR and language technology research.
+
+---
+
+# License
+
+This dataset is released under the CC BY 4.0 license.
+
+The recordings originate from publicly available parliamentary broadcasts from *Løgtingið*, the Parliament of the Faroe Islands.
+
+---
+
+# Contact
+
+**Dávid í Lág**  
+University of the Faroe Islands  
+MSc. Computer Science, Ph.D. student in Computer Science (2024-2028)  
+Research area: Automatic Speech Recognition for Low-Resource Languages