scb10x/thai-tts-intelligiblity-eval

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