Update README.md

README.md

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----
-license: cc-by-nc-4.0
----
+---
+tags:
+- audio
+- speech-recognition
+- neapolitan
+- low-resource
+license: cc-by-nc-4.0
+---
+# Neapolitan-Spoken-Corpus (NSC)
+
+**Neapolitan-Spoken-Corpus (NSC)** is the first publicly available speech corpus designed specifically for benchmarking Automatic Speech Recognition (ASR) systems on Neapolitan, a low-resource Romance dialect of Southern Italy. It includes 141 sentence-level audio recordings along with gold-standard orthographic transcriptions.
+
+The dataset was created to address the lack of computational resources for dialectological research and the development of equitable speech technologies.
+
+## Dataset Description
+
+- **Language:** Neapolitan (ISO 639-3: nap)  
+- **Audio Format:** `.m4a`  
+- **Number of Samples:** 141  
+- **Domains Covered:** Traditional plays, regional poetry, community blogs  
+- **Transcriptions:** Orthographic Neapolitan sentences provided by native speakers  
+- **Ethical Considerations:** All participants provided informed consent; dataset contains no personal or sensitive information.
+
+## Dataset Structure
+```
+Neapolitan-Spoken-Corpus/
+├── audioData/
+│   ├── 002.m4a
+│   ├── 003.m4a
+│   ├── ...
+│   └── 142.m4a
+├── code/
+│   ├── generate_json.py
+│   ├── transcribe_whisper.py
+│   └── evaluate_metrics.py
+├── .gitattributes
+├── README.md
+├── requirements.txt
+└── transcripts.csv
+```
+
+## Intended Uses & Limitations
+
+The dataset is primarily intended for evaluating and developing ASR systems that support dialectal languages, particularly those with minimal computational resources. It provides a benchmark for dialect-aware speech recognition and can also support linguistic research in computational dialectology and language preservation.
+
+## How to Use
+
+To use this dataset and its associated scripts:
+
+```bash
+# Clone repository
+git clone https://huggingface.co/datasets/anonymous-nsc-author/neapolitan-spoken-corpus
+cd neapolitan-spoken-corpus
+# Install dependencies
+pip install -r requirements.txt
+# (Optional) Generate sentences.json
+python code/generate_json.py
+# Transcribe audio files with Whisper ASR (requires OPENAI_API_KEY)
+export OPENAI_API_KEY=your-key-here
+python code/transcribe_whisper.py
+# Evaluate transcription accuracy metrics (WER, BLEU, etc.)
+python code/evaluate_metrics.py
+```
+
+## Evaluation Results
+
+The dataset was evaluated using OpenAI's Whisper model with the language set to Standard Italian. The results indicate significant performance degradation on Neapolitan dialect speech:
+
+| Metric                  | Mean  | Std Dev | Min    | Max    |
+|-------------------------|-------|---------|--------|--------|
+| WER (1 - WER similarity)| 0.1306| 0.1654  | 0.0000 | 0.9091 |
+| Levenshtein (normalized)| 0.6360| 0.1375  | 0.0870 | 0.9804 |
+| BLEU                    | 0.0436| 0.0961  | 0.0000 | 0.8932 |
+| Jaccard                 | 0.1078| 0.1294  | 0.0000 | 0.8333 |
+
+## Ethical Considerations
+
+All participants involved in creating this dataset provided explicit informed consent. Audio and transcription data include no sensitive, private, or personally identifiable information.