# 10 hours data is all you need

arXiv: https://arxiv.org/abs/2210.13067 · HTML: https://mlsanity.com/paper/2210.13067/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [MAC: A unified framework boosting low resource automatic speech recognition](https://mlsanity.com/paper/2302.03498/) (2023)
- [Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators](https://mlsanity.com/paper/2308.15116/) (2023)

## Abstract (arXiv)

We propose a novel procedure to generate pseudo mandarin speech data named as CAMP (character audio mix up), which aims at generating audio from a character scale. We also raise a method for building a mandarin character scale audio database adaptive to CAMP named as META-AUDIO, which makes full use of audio data and can greatly increase the data diversity of the database. Experiments show that our CAMP method is simple and quite effective. For example, we train models with 10 hours of audio data in AISHELL-1 and pseudo audio data generated by CAMP, and achieve a competitive 11.07 character error rate (CER). Besides, we also perform training with only 10 hours of audio data in AIDATATANG dataset and pseudo audio data generated by CAMP, which again achieves a competitive 8.26 CER.