Joycent uses diffusion modeling and conditional layer normalization to synthesize accented speech from standard phones and references, claiming better accentedness and speaker preservation than two-stage baselines.
Few-Shot Accent Synthesis for ASR with LLM-Guided Phoneme Editing
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Synthetic accented speech is a promising way to improve automatic speech recognition (ASR) when real accented recordings are scarce. We ask what makes such data useful for ASR fine-tuning: target-accent phoneme edits that expose the recognizer to accent-specific pronunciations, or random phoneme perturbations that act as augmentation in phoneme space. In a few-shot TTS pipeline, we compare LLM-generated accent edits with matched-rate random substitutions and oracle controls using ground-truth accented phonemes and prosody. Random substitutions recover much of the ASR gain: LLM target-accent edits improve over random by only a small margin, ground-truth phonemes stay close to the random baseline and nearly converge with it as the synthetic ASR fine-tuning set grows larger, and adding ground-truth prosody yields only a modest further gain. Mixing synthetic with real accented speech also stabilizes low-resource fine-tuning, but a fixed synthetic budget can later dilute the information in real data, showing that the real--synthetic ratio matters.
fields
cs.SD 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Random phoneme substitutions recover most ASR gains from synthetic accented speech, with targeted edits and ground-truth prosody providing only marginal additional benefits.
citing papers explorer
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Joycent: Diffusion-based Accent TTS without Accented Phone Prediction
Joycent uses diffusion modeling and conditional layer normalization to synthesize accented speech from standard phones and references, claiming better accentedness and speaker preservation than two-stage baselines.
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Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?
Random phoneme substitutions recover most ASR gains from synthetic accented speech, with targeted edits and ground-truth prosody providing only marginal additional benefits.