A two-stage replay-based post-training method corrects ASR timestamp drift across non-speech gaps while preserving recognition far better than ordinary timestamp fine-tuning.
A self-refining frame- work for enhancing asr using tts-synthesized data
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We propose a self-refining framework that enhances ASR performance with only unlabeled datasets. The process starts with an existing ASR model generating pseudo-labels on unannotated speech, which are then used to train a high-fidelity text-to-speech (TTS) system. Then, synthesized speech text pairs are bootstrapped into the original ASR system, completing the closed-loop self-improvement cycle. We demonstrated the effectiveness of the framework on Taiwanese Mandarin speech. Leveraging 6,000 hours of unlabeled speech, a moderate amount of text data, and synthetic content from the AI models, we adapt Whisper-large-v2 into a specialized model, Twister. Twister reduces error rates by up to 20% on Mandarin and 50% on Mandarin-English code-switching benchmarks compared to Whisper. Results highlight the framework as a compelling alternative to pseudo-labeling self-distillation approaches and provides a practical pathway for improving ASR performance in low-resource or domain-specific settings.
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On synthetic banking speech alone, GRPO cuts ASR WER 40% relative to SFT (36.71%→22.09%) by improving stopping calibration and attention anchoring to audio.
A Taiwan-specific tokenizer, language model, and bridge to a reused acoustic stack cut code-switching TTS CER from 11.45% to 4.81%, with 65.6% listener preference.
Layer-wise pooling plus RIR-augmented synthetic speech matches a 100%-real ASR baseline with only 25% real data (13.6 h) and beats it at higher real fractions.
ASPIRin decouples speaking timing from token content via binary action space projection and applies GRPO with rule-based rewards to optimize interactivity in SLMs without semantic collapse or repetition.
citing papers explorer
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REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
A two-stage replay-based post-training method corrects ASR timestamp drift across non-speech gaps while preserving recognition far better than ordinary timestamp fine-tuning.
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When Synthetic Speech Is All You Have: Better Call GRPO
On synthetic banking speech alone, GRPO cuts ASR WER 40% relative to SFT (36.71%→22.09%) by improving stopping calibration and attention anchoring to audio.
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BlueMagpie-TTS: A Token-Efficient Tokenizer, Language Model, and TTS for Taiwanese-Accent Code-Switching Speech
A Taiwan-specific tokenizer, language model, and bridge to a reused acoustic stack cut code-switching TTS CER from 11.45% to 4.81%, with 65.6% listener preference.
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How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Layer-wise pooling plus RIR-augmented synthetic speech matches a 100%-real ASR baseline with only 25% real data (13.6 h) and beats it at higher real fractions.
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ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models
ASPIRin decouples speaking timing from token content via binary action space projection and applies GRPO with rule-based rewards to optimize interactivity in SLMs without semantic collapse or repetition.