Using Phonemes in cascaded S2S translation pipeline
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Abstract (arXiv)
This paper explores the idea of using phonemes as a textual representation within a conventional multilingual simultaneous speech-to-speech translation pipeline, as opposed to the traditional reliance on text-based language representations. To investigate this, we trained an open-source sequence-to-sequence model on the WMT17 dataset in two formats: one using standard textual representation and the other employing phonemic representation. The performance of both approaches was assessed using the BLEU metric. Our findings shows that the phonemic approach provides comparable quality but offers several advantages, including lower resource requirements or better suitability for low-resource languages.
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References · 8
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
- 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
- 2023StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Modelscited by 152
- 2024Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agendacited by 22
- 2020Reflective-Net: Learning from Explanationscited by 13
- 2024Improving Next Tokens via Second-to-Last Predictions with Generate and Refinecited by 1
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