File size: 6,088 Bytes
6b62405 7aae9d4 6b62405 1bb77f6 6b62405 7aae9d4 6b62405 7aae9d4 6b62405 7aae9d4 6b62405 7aae9d4 6b62405 7aae9d4 6b62405 7aae9d4 6b62405 51a2a85 6b62405 7662d42 6b62405 974b7a3 6b62405 7662d42 4944fa2 6b62405 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
---
license: gpl
task_categories:
- translation
language:
- fa
tags:
- grapheme-to-phoneme
- g2p
- persian
- farsi
- phoneme-translation
- homograph
- mana-tts
- commonvoice
- sentence-bench
- sentencebench
- llm-powered
pretty_name: SentenceBench
size_categories:
- n<1K
---
# Sentence-Bench: A Sentence-Level Benchmarking Dataset for Persian Grapheme-to-Phoneme (G2P) Tasks
## Introduction
**Sentence-Bench** is the first sentence-level benchmarking dataset for evaluating grapheme-to-phoneme (G2P) models in Persian. To the best of our knowledge, no other dataset exists with phoneme-annotated sentences in Persian, designed specifically to address two significant challenges in sentence-level G2P tasks:
1. **Homograph Word Pronunciation**: Predicting the correct pronunciation of homograph words within a sentence.
2. **Context-Sensitive Phonemes**: Predicting context-sensitive phonemes, such as Ezafe, which requires consideration of sentence context.
This dataset allows comprehensive sentence-level evaluation of G2P tools using three metrics:
- **Phoneme Error Rate (PER)**: The conventional evaluation metric for phoneme-level tasks.
- **Homograph Word Accuracy**: Accuracy in predicting the correct pronunciation of homograph words.
The dataset comprises 400 sentences, split into three parts:
- 200 sentences manually constructed using approximately 100 homograph words selected from the **Kaamel** [[1](https://huggingface.co/datasets/MahtaFetrat/KaamelDict)] dictionary, each word appearing in various contexts to showcase multiple pronunciations.
- 100 randomly selected sentences from the unpublished **ManaTTS** [[2](https://huggingface.co/datasets/MahtaFetrat/Mana-TTS)] dataset.
- 100 of the most upvoted sentences from **CommonVoice** [[3](https://commonvoice.mozilla.org/en)].
Each sentence in the first part is annotated with its corresponding phoneme sequence, and sentences containing homograph words include an additional annotation for the correct pronunciation of the homograph within that sentence.
## Dataset Structure
The dataset is provided as a CSV file with the following columns:
- **dataset**: The source of the sentence, which is one of `mana-tts`, `commonvoice`, or `homograph`.
- **grapheme**: The sentence in Persian script.
- **phoneme**: The phonetic transcription of the sentence.
- **homograph word**: The Persian word with ambiguous pronunciation (only for sentences with homographs).
- **pronunciation**: The correct pronunciation of the homograph word within the sentence (only for sentences with homographs).
## Phoneme Representation
The phonetic symbols used in this dataset correspond to Persian phonemes. Below is a reference table for the specific symbols and their IPA equivalents:
| Symbol | Persian Sound | IPA Equivalent | Example |
|:------:|:-------------------:|:--------------:|:--------------------:|
| A | آ, ا (long vowel) | ɑː | ماه: mAh |
| a | َ (short vowel) | æ | درد: dard |
| u | او (long vowel) | uː | دوست: dust |
| i | ای (long vowel) | iː | میز: miz |
| o | ُ (short vowel) | o | ظهر: zohr |
| e | ِ (short vowel) | e | ذهن: zehn |
| S | ش (consonant) | ʃ | شهر: Sahr |
| C | چ (consonant) | tʃʰ | چتر: Catr |
| Z | ژ (consonant) | ʒ | ژاله: ZAle |
| q | غ، ق (consonant) | ɣ, q | غذا: qazA, قند: qand |
| x | خ (consonant) | x | خاک: xAk |
| r | ر (consonant) | ɾ | روح: ruh |
| y | ی (consonant) | j | یار: yAr |
| j | ج (consonant) | dʒ | نجات: nejAt |
| v | و (consonant) | v | ورم: varam |
| ? | ع، ء، ئ (consonant) | ʔ | عمر: ?omr, آینده: ?Ayande |
The Ezafe phones are annotated by `-e` or `-ye` according to the context.
## License
This dataset is released under a GNU license, in accordance with the licenses of its components.
## References
The source datasets can be cited as follows:
```bash
@article{ardila2019common,
title={Common voice: A massively-multilingual speech corpus},
author={Ardila, Rosana and Branson, Megan and Davis, Kelly and Henretty, Michael and Kohler, Michael and Meyer, Josh and Morais, Reuben and Saunders, Lindsay and Tyers, Francis M and Weber, Gregor},
journal={arXiv preprint arXiv:1912.06670},
year={2019}
}
```
```bash
@inproceedings{qharabagh-etal-2025-manatts,
title = "{M}ana{TTS} {P}ersian: a recipe for creating {TTS} datasets for lower resource languages",
author = "Qharabagh, Mahta Fetrat and Dehghanian, Zahra and Rabiee, Hamid R.",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.464/",
pages = "9177--9206",
}
```
## Contact
For any questions or inquiries, feel free to open an issue or contact the author at [m.fetrat@sharif.edu].
## Citation
Please cite the following papers if you use this dataset:
```bash
@inproceedings{qharabagh2025llm,
title={LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study},
author={Qharabagh, Mahta Fetrat and Dehghanian, Zahra and Rabiee, Hamid R},
booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2025},
organization={IEEE}
}
```
|