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New TTS pipeline enhances ASR systems with phoneme-based augmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a unified pipeline for generating synthetic speech to improve automatic speech recognition (ASR) systems. This pipeline utilizes a multilingual text-to-speech (TTS) model, F5-TTS, with language-ID conditioning. A novel method called phoneme-frequency-guided selection (PFGS) ranks candidate sentences based on phoneme frequencies, outperforming random selection and real-only training in experiments across Arabic, French, Italian, and Portuguese. AI

IMPACT This research could lead to more efficient and effective training of ASR systems, particularly in low-resource languages, by leveraging synthetic data.

RANK_REASON The cluster contains an academic paper detailing a new method and pipeline for TTS-to-ASR augmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TTS pipeline enhances ASR systems with phoneme-based augmentation

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The cluster contains an academic paper detailing a new method and pipeline for TTS-to-ASR augmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhen Wang, TianRui Wu, RongQi Han, Hao Wu, Wei Liang ·

    Scaling phoneme-based TTS augmentation for ASR: A unified pipeline and controlled study

    arXiv:2608.26697v1 Announce Type: new Abstract: Synthetic speech provides scalable supervision for automatic speech recognition (ASR), but its benefit depends on the selected texts, reference speech, and amount of synthesized data. We present a unified phoneme-based TTS-to-ASR au…

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