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---
dataset_info:
features:
- name: id
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: all
num_bytes: 91813
num_examples: 290
- name: easy
num_bytes: 9124
num_examples: 50
- name: medium
num_bytes: 20234
num_examples: 50
- name: hard
num_bytes: 27971
num_examples: 50
- name: scbx
num_bytes: 17314
num_examples: 50
- name: name
num_bytes: 10118
num_examples: 50
- name: other
num_bytes: 7052
num_examples: 40
download_size: 103240
dataset_size: 183626
configs:
- config_name: default
data_files:
- split: all
path: data/all-*
- split: easy
path: data/easy-*
- split: medium
path: data/medium-*
- split: hard
path: data/hard-*
- split: scbx
path: data/scbx-*
- split: name
path: data/name-*
- split: other
path: data/other-*
---
# Thai-TTS-Intelligibility-Eval
**Thai-TTS-Intelligibility-Eval** is a curated evaluation set for measuring **intelligibility** of Thai Text-to-Speech (TTS) systems.
All 290 items are short, challenging phrases that commonly trip up phoneme-to-grapheme converters, prosody models, or pronunciation lexicons.
It is **not** intended for training; use it purely for benchmarking and regression tests.
## Dataset Summary
| Split | #Utterances | Description |
|---------|-------------|-------------------------------------------------------------|
| `easy` | 50 | Everyday phrases that most TTS systems should read correctly|
| `medium`| 50 | More challening than easy |
| `hard` | 50 | Hard phrases, e.g., mixed Thai and English and unique names |
| `scbx` | 50 | SCBX-specific terminology, products, and names |
| `name` | 50 | Synthetic Thai personal names (mixed Thai & foreign roots) |
| `other` | 40 | Miscellaneous edge-cases not covered above |
| **Total** | **290** | |
Each record contains:
- **`id`** `string` Unique identifier
- **`text`** `string` sentence/phrase
- **`category`** `string` One of *easy, medium, hard, scbx, name, other*
## Loading With 🤗 `datasets`
```python
from datasets import load_dataset
ds = load_dataset(
"scb10x/thai-tts-intelligiblity-eval",
)
ds_scbx = ds["scbx"]
print(ds[0])
# {'id': '53ef39464d9c1e6f', 'text': '...', 'category': 'scbx'}
```
## Intended Use
1. **Objective evaluation**
- *Compute WER/CER* between automatic transcripts of your TTS output and the gold reference text.
- Code: https://github.com/scb-10x/thai-tts-eval/tree/main/intelligibility
2. **Subjective evaluation**
- Conduct human listening tests (MOS, ABX, etc.)—the dataset is small enough for quick rounds.
- Future work
4. **Regression testing**
- Track intelligibility across model versions with a fixed set of hard sentences.
- Future work
## CER Evaluation Results
- CER: lower is better
| System | All | Easy | Medium | Hard | SCBX | Name | Other |
|-----------------------------------|------|-------|--------|------|------|-------|-------|
| Azure Premwadee | 9.39 | 2.87 | 2.92 | 13.80| 10.44| 13.07 | 7.57 |
| `facebook-mms-tts-tha` | 28.47| 10.31 | 12.40 | 38.83| 36.04| 26.33 | 30.83 |
| `VIZINTZOR-MMS-TTS-THAI-FEMALEV1` | 27.42| 13.30 | 13.13 | 30.92| 34.76| 25.53 | 54.60 |
|