# Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition

arXiv: https://arxiv.org/abs/1904.08031 · HTML: https://mlsanity.com/paper/1904.08031/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Robust Multi-Task Learning with Excess Risks](https://mlsanity.com/paper/2402.02009/) (2024)
- [The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization](https://mlsanity.com/paper/2204.04384/) (2022)
- [DIVA: Dataset Derivative of a Learning Task](https://mlsanity.com/paper/2111.09785/) (2021)
- [The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning](https://mlsanity.com/paper/2310.00141/) (2023)
- [Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis](https://mlsanity.com/paper/2306.14701/) (2023)
- [Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy](https://mlsanity.com/paper/2309.01903/) (2023)

## Abstract (arXiv)

It is an effective way that improves the performance of the existing Automatic Speech Recognition (ASR) systems by retraining with more and more new training data in the target domain. Recently, Deep Neural Network (DNN) has become a successful model in the ASR field. In the training process of the DNN based methods, a back propagation of error between the transcription and the corresponding annotated text is used to update and optimize the parameters. Thus, the parameters are more influenced by the training samples with a big propagation error than the samples with a small one. In this paper, we define the samples with significant error as the hard samples and try to improve the performance of the ASR system by adding many of them. Unfortunately, the hard samples are sparse in the training data of the target domain, and manually label them is expensive. Therefore, we propose a hard samples mining method based on an enhanced deep multiple instance learning, which can find the hard samples from unlabeled training data by using a small subset of the dataset with manual labeling in the target domain. We applied our method to an End2End ASR task and obtained the best performance.