Document TSV and thesis context

README.md

@@ -26,14 +26,51 @@ The dataset is useful for quick experiments with Swedish ASR systems, especially
 when you want to inspect recognition quality on spontaneous speech from
 different regions, speakers, ages, and genders.
 
+## Thesis Context
+
+This dataset was prepared as part of Kajsa Vesterberg's bachelor thesis,
+[*Lost in Translation: AI's Struggles with Scanian - A Study on Language Models'
+Attempts to Conquer Swedish and its Dialects*](https://lup.lub.lu.se/student-papers/search/publication/9191345),
+published through Lund University's LUP Student Papers repository in 2025.
+
+The thesis investigates whether automatic speech recognition performs unevenly
+across Swedish regional speech, with particular attention to Scanian dialects.
+
 ## Repository Contents
 
 | File | Description |
 | --- | --- |
+| `swedia_asr_dataset.tsv` | Normalized row-level TSV with reference transcripts, ASR outputs, and computed WER/CER. |
 | `Dataset-Swedia-2000.docx` | ASR/dictation output grouped by location and speaker category. |
 | `Original-Texts-Swedia-2000.docx` | Manually corrected reference transcriptions for the same speech excerpts. |
 | `Calculation-of-Results-Swedia-2000.xlsx` | Spreadsheet with WER/CER calculations and comparison notes. |
 
+## TSV Format
+
+`swedia_asr_dataset.tsv` is the easiest file to use programmatically. It has 56
+rows, one row per location and speaker-group excerpt.
+
+Columns:
+
+- `location`
+- `speaker_group`
+- `reference_transcript`
+- `word_diktering_transcript`
+- `word_diktering_wer`
+- `word_diktering_cer`
+- `whisper_transcript`
+- `whisper_wer`
+- `whisper_cer`
+
+Some `word_diktering_*` fields are empty because the Word Dictation section does
+not contain every location/speaker combination that appears in the reference and
+Whisper sections.
+
+The original DOCX and XLSX files are intentionally kept in the repository. They
+represent the source working documents from the thesis project, while
+`swedia_asr_dataset.tsv` is a cleaned, analysis-friendly version derived from
+them.
+
 ## Speaker and Location Structure
 
 The documents are organized by location and speaker group. Examples of locations
@@ -60,10 +97,13 @@ Speaker groups include:
 
 ## Metrics
 
-The spreadsheet includes evaluation fields for:
+The normalized TSV includes computed evaluation fields for:
 
 - `WER`: word error rate
 - `CER`: character error rate
+
+The original spreadsheet also includes comparison fields for:
+
 - comparisons across locations/regions
 - comparisons across speaker age and gender groups
 - Word Dictation vs. Whisper-style ASR comparisons
@@ -74,32 +114,30 @@ The spreadsheet includes evaluation fields for:
 - Compare recognition quality across dialect regions.
 - Inspect typical ASR errors in spontaneous Swedish speech.
 - Practice WER/CER calculation and ASR error analysis.
+- Load a single TSV instead of manually extracting the DOCX files.
 
 ## Loading the Files
 
-The files are currently distributed as Office documents rather than a single
-normalized CSV/JSON dataset. For analysis, download the DOCX/XLSX files and
-extract the relevant text and metrics with tools such as `python-docx`,
-`openpyxl`, LibreOffice, or spreadsheet software.
+For most analysis, start with the normalized TSV:
 
 Example:
 
 ```python
-from huggingface_hub import hf_hub_download
+import pandas as pd
 
-path = hf_hub_download(
-    repo_id="kvest/Swedia-ASR-Dataset",
-    repo_type="dataset",
-    filename="Calculation-of-Results-Swedia-2000.xlsx",
+data = pd.read_csv(
+    "hf://datasets/kvest/Swedia-ASR-Dataset/swedia_asr_dataset.tsv",
+    sep="\t",
 )
-print(path)
+print(data[["location", "speaker_group", "whisper_wer", "whisper_cer"]].head())
 ```
 
+The original DOCX/XLSX files are still included for transparency and manual
+inspection.
+
 ## Limitations
 
 - The dataset is small and best suited for exploratory ASR evaluation.
-- The files are document-based, not yet normalized into row-level machine
-  learning tables.
 - The repository contains transcriptions and evaluation material, not raw audio.
 - Results should be interpreted as a compact benchmark/sample rather than a
   comprehensive Swedish ASR evaluation suite.
@@ -107,6 +145,6 @@ print(path)
 ## Citation and Source Notes
 
 This dataset is based on transcribed material from Swedia 2000 and was prepared
-for ASR comparison and error analysis. If you use it, cite this dataset page and
-also acknowledge Swedia 2000 as the underlying transcription source where
-appropriate.
+for ASR comparison and error analysis in the bachelor thesis linked above. If
+you use it, cite this dataset page, the thesis, and Swedia 2000 as the
+underlying transcription source where appropriate.