mlsanityData to 7 Oct 2026

Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech

arXiv 2401.10465 ↗First posted 19 Jan 2024cs.CL cs.SD eess.AScited by 3

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Abstract (arXiv)

Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely on carefully hand-crafted lexicons developed by experts. This poses a two-fold problem. Firstly, the lexicons are generated using a fixed phoneme set, usually, ARPABET or IPA, which might not be the most optimal way to represent phonemes for all languages. Secondly, the man-hours required to produce such an expert lexicon are very high. In this paper, we eliminate both of these issues by using recent advances in self-supervised learning to obtain data-driven phoneme representations instead of fixed representations. We compare our lexicon-free approach against strong baselines that utilize a well-crafted lexicon. Furthermore, we show that our data-driven lexicon-free method performs as good or even marginally better than the conventional rule-based or lexicon-based neural G2Ps in terms of Mean Opinion Score (MOS) while using no prior language lexicon or phoneme set, i.e. no linguistic expertise.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 19 Jan 2024.

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13 of 25 references.

References · 13

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  5. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
  6. 2022NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Qualitycited by 151
  7. 2020Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesiscited by 54
  8. 2022ByT5 model for massively multilingual grapheme-to-phoneme conversioncited by 49
  9. 2020Transformer based Grapheme-to-Phoneme Conversioncited by 32
  10. 2015Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversioncited by 19
  11. 2021Streaming end-to-end speech recognition with jointly trained neural feature enhancementcited by 4
  12. 2021A comparison of streaming models and data augmentation methods for robust speech recognitioncited by 4
1 more
  1. 2019Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Modelscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Exploring the Benefits of Tokenization of Discrete Acoustic Unitscited by 4
  2. 2025LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Contextcited by 3
  3. 2026Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speechcited by 0