# Dynamic curriculum learning via data parameters for noise robust keyword spotting

arXiv: https://arxiv.org/abs/2102.09666 · HTML: https://mlsanity.com/paper/2102.09666/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models](https://mlsanity.com/paper/1710.09617/) (2017)
- [A Cascade Architecture for Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1712.03603/) (2017)

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

- [Comparison and Analysis of New Curriculum Criteria for End-to-End ASR](https://mlsanity.com/paper/2208.05782/) (2022)
- [Curriculum optimization for low-resource speech recognition](https://mlsanity.com/paper/2202.08883/) (2022)
- [Streaming Anchor Loss: Augmenting Supervision with Temporal Significance](https://mlsanity.com/paper/2310.05886/) (2023)
- [Pre-trained Language Models as Re-Annotators](https://mlsanity.com/paper/2205.05368/) (2022)
- [Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation](https://mlsanity.com/paper/2608.19766/) (2026)

## Abstract (arXiv)

We propose dynamic curriculum learning via data parameters for noise robust keyword spotting. Data parameter learning has recently been introduced for image processing, where weight parameters, so-called data parameters, for target classes and instances are introduced and optimized along with model parameters. The data parameters scale logits and control importance over classes and instances during training, which enables automatic curriculum learning without additional annotations for training data. Similarly, in this paper, we propose using this curriculum learning approach for acoustic modeling, and train an acoustic model on clean and noisy utterances with the data parameters. The proposed approach automatically learns the difficulty of the classes and instances, e.g. due to low speech to noise ratio (SNR), in the gradient descent optimization and performs curriculum learning. This curriculum learning leads to overall improvement of the accuracy of the acoustic model. We evaluate the effectiveness of the proposed approach on a keyword spotting task. Experimental results show 7.7% relative reduction in false reject ratio with the data parameters compared to a baseline model which is simply trained on the multiconditioned dataset.