HUI-Audio-Corpus-German：一个高质量的TTS数据集
HUI-Audio-Corpus-German: A high quality TTS dataset
摘要：
互联网上音频数据的可用性不断增加，导致了大量的数据集用于基于神经网络的文本到语音应用程序的开发和训练。语音质量差异很大、采样率低、缺乏文本规范化以及音频样本与相应转录句子的不利对齐仍然限制了在此任务上训练的深度神经网络的性能。此外，德语等语言的数据资源仍然非常有限。我们介绍了“HUI-Audio-Corpus-German”，这是一个用于TTS引擎的大型开源数据集，使用处理管道创建，可以生成高质量的音频到转录对齐，并减少创建所需的手动工作。
英文摘要：
The increasing availability of audio data on the internet lead to a multitude of datasets for development and training of text to speech applications, based on neural networks. Highly differing quality of voice, low sampling rates, lack of text normalization and disadvantageous alignment of audio samples to corresponding transcript sentences still limit the performance of deep neural networks trained on this task. Additionally, data resources in languages like German are still very limited. We introduce the "HUI-Audio-Corpus-German", a large, open-source dataset for TTS engines, created with a processing pipeline, which produces high quality audio to transcription alignments and decreases manual effort needed for creation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
