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  1. GE2PE: persian end-to-end grapheme-to-phoneme conversion
GE2PE: persian end-to-end grapheme-to-phoneme conversion
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GE2PE: persian end-to-end grapheme-to-phoneme conversion

Rahmati, E

GE2PE: persian end-to-end grapheme-to-phoneme conversion

Rahmati, E ; Sharif University of Technology | 2024

93 Viewed
  1. Type of Document: Article
  2. DOI: 10.18653/v1/2024.findings-emnlp.196
  3. Publisher: ACL Anathomy , 2024
  4. Abstract:
  5. Text-to-Speech (TTS) systems have made significant strides, enabling the generation of speech from grapheme sequences. However, for low-resource languages, these models still struggle to produce natural and intelligible speech. Grapheme-to-Phoneme conversion (G2P) addresses this challenge by enhancing the input sequence with phonetic information. Despite these advancements, existing G2P systems face limitations when dealing with Persian texts due to the complexity of Persian transcription. In this study, we focus on enriching resources for the Persian language. To achieve this, we introduce two novel G2P training datasets: one manually labeled and the other machine-generated. These datasets comprise over five million sentences alongside their corresponding phoneme sequences. Additionally, we propose two evaluation datasets tailored for Persian sub-tasks, including Kasre-Ezafe detection, homograph disambiguation, and handling out-of-vocabulary (OOV) words. To tackle the unique challenges of the Persian language, we develop a new sentence-level End-to-End (E2E) model leveraging a two-step training approach, as outlined in our paper, to maximize the impact of manually labeled data. The results show that our model surpasses the state-of-the-art performance by 1.86% in word error rate, 4.03% in Kasre-Ezafe detection recall, and 3.42% in homograph disambiguation accuracy. © 2024 Association for Computational Linguistics
  6. Keywords:
  7. Speech recognition ; End to end ; Grapheme-to-phoneme conversion ; Homograph disambiguations ; Input sequence ; Low resource languages ; Persian languages
  8. Source: EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024 ; 2024 , Pages 3426-3436 ; 979-889176168-1 (ISBN)
  9. URL: https://aclanthology.org/2024.findings-emnlp.196