# AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data

arXiv: https://arxiv.org/abs/2309.13905 · HTML: https://mlsanity.com/paper/2309.13905/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2023.

## Built on by (most cited first, 16 of 16)

- [Kimi-Audio Technical Report](https://mlsanity.com/paper/2504.18425/) (2025)
- [Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation](https://mlsanity.com/paper/2407.05361/) (2024)
- [MiMo-Audio: Audio Language Models are Few-Shot Learners](https://mlsanity.com/paper/2512.23808/) (2025)
- [LeVo: High-Quality Song Generation with Multi-Preference Alignment](https://mlsanity.com/paper/2506.07520/) (2025)
- [Preference Alignment Improves Language Model-Based TTS](https://mlsanity.com/paper/2409.12403/) (2024)
- [Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation](https://mlsanity.com/paper/2501.15907/) (2025)
- [MoonCast: High-Quality Zero-Shot Podcast Generation](https://mlsanity.com/paper/2503.14345/) (2025)
- [VIBEVOICE-ASR Technical Report](https://mlsanity.com/paper/2601.18184/) (2026)
- [CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations](https://mlsanity.com/paper/2404.06690/) (2024)
- [TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch](https://mlsanity.com/paper/2412.08237/) (2024)
- [SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription](https://mlsanity.com/paper/2509.17404/) (2025)
- [Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection](https://mlsanity.com/paper/2506.00955/) (2025)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [Teochew-Wild: The First In-the-wild Teochew Dataset with Orthographic Annotations](https://mlsanity.com/paper/2505.05056/) (2025)
- [Voice Adaptation for Swiss German](https://mlsanity.com/paper/2505.22054/) (2025)
- [TISDiSS: A Training-Time and Inference-Time Scalable Framework for Discriminative Source Separation](https://mlsanity.com/paper/2509.15666/) (2025)

## Abstract (arXiv)

Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild large-scale speech data in the speech technology community remains constrained. One reason for this limitation is that a considerable amount of the publicly available speech data is compromised by background noise, speech overlapping, lack of speech segmentation information, missing speaker labels, and incomplete transcriptions, which can largely hinder their usefulness. On the other hand, human annotation of speech data is both time-consuming and costly. To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically. The proposed AutoPrep framework comprises six components: speech enhancement, speech segmentation, speaker clustering, target speech extraction, quality filtering and automatic speech recognition. Experiments conducted on the open-sourced WenetSpeech and our self-collected AutoPrepWild corpora demonstrate that the proposed AutoPrep framework can generate preprocessed data with similar DNSMOS and PDNSMOS scores compared to several open-sourced TTS datasets. The corresponding TTS system can achieve up to 0.68 in-domain speaker similarity.