| Challenge: | Existing text-to-speech systems struggle to produce natural speech from grapheme sequences . Grapheme-to phoneme conversion (G2P) systems face limitations when dealing with Persian texts due to the complexity of Persian transcription. |
| Approach: | They propose to use phonetic information to enhance the input sequence for Persian translations. |
| Outcome: | The proposed model surpasses state-of-the-art models by 1.86% in word error rate and 3.42% in homograph disambiguation accuracy. |
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| Challenge: | specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications. |
| Approach: | They propose a semi-automated pipeline for constructing homograph-focused datasets . they introduce a HomoRich dataset and advocate for a paradigm shift . |
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Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages (D19-61)
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| Challenge: | Grapheme-to-phoneme conversion (g2p) is a task of predicting the pronunciation of words from their orthographic representation. |
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Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language (N19-1)
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Sudipta Saha Shubha, Nafis Sadeq, Shafayat Ahmed, Md. Nahidul Islam, Muhammad Abdullah Adnan, Md. Yasin Ali Khan, Mohammad Zuberul Islam
| Challenge: | Existing methods for Grapheme to phoneme conversion in Bangla language are mostly rule-based. |
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Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT (2024.lrec-main)
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Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
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Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble (2022.findings-acl)
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| Challenge: | Existing work focuses on low-resource and endangered languages with limited training sets. |
| Approach: | They propose a hypothesis set for any unseen target language and combine it with a confusion network to propose 'the most likely hypothesis' they test the approach on over 600 unseened languages and demonstrate it significantly outperforms baselines. |
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ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages (2025.naacl-long)
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| Challenge: | a new text-to-speech system is needed for visual impairments and the visually impaired . a text-based system is not available for all users, and is therefore limited to a limited audience. |
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Hamid Jahad Sarvestani, Vida Ramezanian, Saee Saadat, Neda Taghizadeh Serajeh, Maryam Sadat Razavi Taheri, Shohreh Kasaei, MohammadAmin Fazli, Ehsaneddin Asgari
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Epitran: Precision G2P for Many Languages (L18-1)
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| Challenge: | Epitran is a multilingual, multi-back-end system for grapheme-to-phoneme transduction . it supports 61 languages and is open source under an MIT license . |
| Approach: | Epitran is a multilingual back-end system for grapheme-to-phoneme transduction . it takes word tokens in the orthography of a language and outputs a phonemic representation . Epitran's efficacy has been demonstrated in multiple research projects . |
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Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation (P19-1)
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| Challenge: | Previous work on end-to-end translation from speech uses frame-level features as speech representations, which creates longer, sparser sequences than text. |
| Approach: | They propose a method to generate compressed phoneme-like speech representations that generate shorter, higher-level source sequences for translation. |
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An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation (2024.lrec-main)
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Ahmet Gunduz, Kamer Ali Yuksel, Kareem Darwish, Golara Javadi, Fabio Minazzi, Nicola Sobieski, Sébastien Bratières
| Challenge: | Text-to-speech (TTS) models require data availability and quality of training data. |
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