The paper introduces LLM-Dys, a 12,790-hour synthetic dysfluent speech corpus generated by LLM plus TTS, and claims state-of-the-art dysfluency detection with a Whisper-based transcriber.
We divide dysfluency detection into word and phoneme levels
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Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection
The paper introduces LLM-Dys, a 12,790-hour synthetic dysfluent speech corpus generated by LLM plus TTS, and claims state-of-the-art dysfluency detection with a Whisper-based transcriber.