# Comparison and Analysis of New Curriculum Criteria for End-to-End ASR

arXiv: https://arxiv.org/abs/2208.05782 · HTML: https://mlsanity.com/paper/2208.05782/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 51 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Automated Curriculum Learning for Neural Networks](https://mlsanity.com/paper/1704.03003/) (2017)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [Learning to Execute](https://mlsanity.com/paper/1410.4615/) (2014)
- [Dilated Recurrent Neural Networks](https://mlsanity.com/paper/1710.02224/) (2017)
- [A Better and Faster End-to-End Model for Streaming ASR](https://mlsanity.com/paper/2011.10798/) (2020)
- [Improved training of end-to-end attention models for speech recognition](https://mlsanity.com/paper/1805.03294/) (2018)
- [A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition](https://mlsanity.com/paper/1606.06864/) (2016)
- [Streaming Multi-speaker ASR with RNN-T](https://mlsanity.com/paper/2011.11671/) (2020)
- [Toward Fairness in AI for People with Disabilities: A Research Roadmap](https://mlsanity.com/paper/1907.02227/) (2019)
- [Improved training for online end-to-end speech recognition systems](https://mlsanity.com/paper/1711.02212/) (2017)
- [ConvMixer: Feature Interactive Convolution with Curriculum Learning for Small Footprint and Noisy Far-field Keyword Spotting](https://mlsanity.com/paper/2201.05863/) (2022)
- [Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection](https://mlsanity.com/paper/1711.03689/) (2017)
- [Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks](https://mlsanity.com/paper/2203.12906/) (2022)
- [Dynamic curriculum learning via data parameters for noise robust keyword spotting](https://mlsanity.com/paper/2102.09666/) (2021)
- [Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios](https://mlsanity.com/paper/2104.02232/) (2021)
- [Curriculum optimization for low-resource speech recognition](https://mlsanity.com/paper/2202.08883/) (2022)

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

- [Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning](https://mlsanity.com/paper/2411.00973/) (2024)
- [Tone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition](https://mlsanity.com/paper/2606.31642/) (2026)

## Abstract (arXiv)

It is common knowledge that the quantity and quality of the training data play a significant role in the creation of a good machine learning model. In this paper, we take it one step further and demonstrate that the way the training examples are arranged is also of crucial importance. Curriculum Learning is built on the observation that organized and structured assimilation of knowledge has the ability to enable faster training and better comprehension. When humans learn to speak, they first try to utter basic phones and then gradually move towards more complex structures such as words and sentences. This methodology is known as Curriculum Learning, and we employ it in the context of Automatic Speech Recognition. We hypothesize that end-to-end models can achieve better performance when provided with an organized training set consisting of examples that exhibit an increasing level of difficulty (i.e. a curriculum). To impose structure on the training set and to define the notion of an easy example, we explored multiple scoring functions that either use feedback from an external neural network or incorporate feedback from the model itself. Empirical results show that with different curriculums we can balance the training times and the network's performance.