# Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition

arXiv: https://arxiv.org/abs/1910.07323 · HTML: https://mlsanity.com/paper/1910.07323/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Star Temporal Classification: Sequence Classification with Partially Labeled Data](https://mlsanity.com/paper/2201.12208/) (2022)
- [Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2308.06547/) (2023)
- [Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask](https://mlsanity.com/paper/2110.04056/) (2021)
- [Investigation of Training Label Error Impact on RNN-T](https://mlsanity.com/paper/2112.00350/) (2021)
- [SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale](https://mlsanity.com/paper/2211.11740/) (2022)

## Abstract (arXiv)

The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions. Based on a noise model of transcription errors, Lead2Gold searches for better transcriptions of the training data with a beam search that takes this noise model into account. The beam search is differentiable and does not require a forced alignment step, thus the whole system is trained end-to-end. Lead2Gold can be viewed as a new loss function that can be used on top of any sequence-to-sequence deep neural network. We conduct proof-of-concept experiments on noisy transcriptions generated from letter corruptions with different noise levels. We show that Lead2Gold obtains a better ASR accuracy than a competitive baseline which does not account for the (artificially-introduced) transcription noise.