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Creating synthetic voices with found data is challenging, as real-worldrecordings often contain various types of audio degradation. One way to addressthis problem is to pre-enhance the speech with an enhancement model and thenuse the enhanced data for text-to-speech (TTS) model training. This paperinvestigates the use of conditional diffusion models for generalized speechenhancement, which aims at addressing multiple types of audio degradationsimultaneously. The enhancement is performed on the log Mel-spectrogram domainto align with the TTS training objective. Text information is introduced as anadditional condition to improve the model robustness. Experiments on real-worldrecordings demonstrate that the synthetic voice built on data enhanced by theproposed model produces higher-quality synthetic speech, compared to thosetrained on data enhanced by strong baselines. Code and pre-trained parametersof the proposed enhancement model are available at\url{https://github.com/dmse4tts/DMSE4TTS}

Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data