# Investigation of Training Label Error Impact on RNN-T

arXiv: https://arxiv.org/abs/2112.00350 · HTML: https://mlsanity.com/paper/2112.00350/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Scaling Laws for Autoregressive Generative Modeling](https://mlsanity.com/paper/2010.14701/) (2020)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [Decoupling "when to update" from "how to update"](https://mlsanity.com/paper/1706.02613/) (2017)
- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models](https://mlsanity.com/paper/1903.02852/) (2019)
- [Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition](https://mlsanity.com/paper/1910.07323/) (2019)
- [Exploring Methods for the Automatic Detection of Errors in Manual Transcription](https://mlsanity.com/paper/1904.04294/) (2019)

## Built on by (most cited first, 3 of 3)

- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2308.06547/) (2023)
- [Human Transcription Quality Improvement](https://mlsanity.com/paper/2309.14372/) (2023)

## Abstract (arXiv)

In this paper, we propose an approach to quantitatively analyze impacts of different training label errors to RNN-T based ASR models. The result shows deletion errors are more harmful than substitution and insertion label errors in RNN-T training data. We also examined label error impact mitigation approaches on RNN-T and found that, though all the methods mitigate the label-error-caused degradation to some extent, they could not remove the performance gap between the models trained with and without the presence of label errors. Based on the analysis results, we suggest to design data pipelines for RNN-T with higher priority on reducing deletion label errors. We also find that ensuring high-quality training labels remains important, despite of the existence of the label error mitigation approaches.