Phonikud: Hebrew Grapheme-to-Phoneme Conversion for Real-Time Text-to-Speech

• URL: http://arxiv.org/abs/2506.12311v1
• Date: Sat, 14 Jun 2025 02:16:38 GMT
• Title: Phonikud: Hebrew Grapheme-to-Phoneme Conversion for Real-Time Text-to-Speech
• Authors: Yakov Kolani, Maxim Melichov, Cobi Calev, Morris Alper,
• Abstract summary: Phonikud is a lightweight, open-source Hebrew grapheme-to-phoneme (G2P) system that outputs fully-specified IPA transcriptions.<n>We contribute the ILSpeech dataset of transcribed Hebrew speech with IPA annotations, serving as a benchmark for Hebrew G2P and as training data for TTS systems.
• Score: 1.3124513975412255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time text-to-speech (TTS) for Modern Hebrew is challenging due to the language's orthographic complexity. Existing solutions ignore crucial phonetic features such as stress that remain underspecified even when vowel marks are added. To address these limitations, we introduce Phonikud, a lightweight, open-source Hebrew grapheme-to-phoneme (G2P) system that outputs fully-specified IPA transcriptions. Our approach adapts an existing diacritization model with lightweight adaptors, incurring negligible additional latency. We also contribute the ILSpeech dataset of transcribed Hebrew speech with IPA annotations, serving as a benchmark for Hebrew G2P and as training data for TTS systems. Our results demonstrate that Phonikud G2P conversion more accurately predicts phonemes from Hebrew text compared to prior methods, and that this enables training of effective real-time Hebrew TTS models with superior speed-accuracy trade-offs. We release our code, data, and models at https://phonikud.github.io.

Related papers

• LLM-based phoneme-to-grapheme for phoneme-based speech recognition [11.552927239284582]
  We propose phoneme-to-grapheme (LLM-P2G) decoding for phoneme-based automatic speech recognition (ASR)<n>Our experimental results show that LLM-P2G outperforms WFST-based systems in crosslingual ASR for Polish and German, by relative WER reductions of 3.6% and 6.9% respectively.
  arXiv  Detail & Related papers  (2025-06-05T07:35:55Z)
• A Language Modeling Approach to Diacritic-Free Hebrew TTS [21.51896995655732]
  We tackle the task of text-to-speech (TTS) in Hebrew. Traditional Hebrew contains Diacritics, which dictate the way individuals should pronounce given words. The lack of diacritics in modern Hebrew results in readers expected to conclude the correct pronunciation.
  arXiv  Detail & Related papers  (2024-07-16T22:43:49Z)
• T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text [59.57676466961787]
  We propose a novel dynamic vector quantization (DVA-VAE) model that can adjust the encoding length based on the information density in sign language. Experiments conducted on the PHOENIX14T dataset demonstrate the effectiveness of our proposed method. We propose a new large German sign language dataset, PHOENIX-News, which contains 486 hours of sign language videos, audio, and transcription texts.
  arXiv  Detail & Related papers  (2024-06-11T10:06:53Z)
• Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding [55.989376102986654]
  This paper studies a transferable phoneme embedding framework that aims to deal with the cross-lingual text-to-speech problem under the few-shot setting. We propose a framework that consists of a phoneme-based TTS model and a codebook module to project phonemes from different languages into a learned latent space.
  arXiv  Detail & Related papers  (2022-06-27T11:24:40Z)
• Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech [88.22544315633687]
  Polyphone disambiguation aims to capture accurate pronunciation knowledge from natural text sequences for reliable Text-to-speech systems. We propose Dict-TTS, a semantic-aware generative text-to-speech model with an online website dictionary. Experimental results in three languages show that our model outperforms several strong baseline models in terms of pronunciation accuracy.
  arXiv  Detail & Related papers  (2022-06-05T10:50:34Z)
• Textless Speech-to-Speech Translation on Real Data [49.134208897722246]
  We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language. We tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data.
  arXiv  Detail & Related papers  (2021-12-15T18:56:35Z)
• AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data [115.38309338462588]
  We develop AdaSpeech 2, an adaptive TTS system that only leverages untranscribed speech data for adaptation. Specifically, we introduce a mel-spectrogram encoder to a well-trained TTS model to conduct speech reconstruction. In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.
  arXiv  Detail & Related papers  (2021-04-20T01:53:30Z)
• AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With [7.345047237652976]
  Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology. While advances reported for English using PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far between.
  arXiv  Detail & Related papers  (2021-04-08T20:51:29Z)
• Consecutive Decoding for Speech-to-text Translation [51.155661276936044]
  COnSecutive Transcription and Translation (COSTT) is an integral approach for speech-to-text translation. The key idea is to generate source transcript and target translation text with a single decoder. Our method is verified on three mainstream datasets.
  arXiv  Detail & Related papers  (2020-09-21T10:10:45Z)
• RECOApy: Data recording, pre-processing and phonetic transcription for end-to-end speech-based applications [4.619541348328938]
  RECOApy streamlines the steps of data recording and pre-processing required in end-to-end speech-based applications. The tool implements an easy-to-use interface for prompted speech recording, spectrogram and waveform analysis, utterance-level normalisation and silence trimming. The grapheme-to-phoneme (G2P) converters are deep neural network (DNN) based architectures trained on lexicons extracted from the Wiktionary online collaborative resource.
  arXiv  Detail & Related papers  (2020-09-11T15:26:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.