README.md

metadata

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

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
3. 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