# HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words

arXiv: https://arxiv.org/abs/2210.15425 · HTML: https://mlsanity.com/paper/2210.15425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [End-to-End Streaming Keyword Spotting](https://mlsanity.com/paper/1812.02802/) (2018)
- [A Cascade Architecture for Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1712.03603/) (2017)
- [Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric](https://mlsanity.com/paper/2011.01151/) (2020)
- [Stacked 1D convolutional networks for end-to-end small footprint voice trigger detection](https://mlsanity.com/paper/2008.03405/) (2020)
- [Online Keyword Spotting with a Character-Level Recurrent Neural Network](https://mlsanity.com/paper/1512.08903/) (2015)
- [An End-to-End Architecture for Keyword Spotting and Voice Activity Detection](https://mlsanity.com/paper/1611.09405/) (2016)

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

- [A Literature Review of Keyword Spotting Technologies for Urdu](https://mlsanity.com/paper/2409.16317/) (2024)
- [Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU](https://mlsanity.com/paper/2506.08911/) (2025)
- [RepCNN: Micro-sized, Mighty Models for Wakeword Detection](https://mlsanity.com/paper/2406.02652/) (2024)

## Abstract (arXiv)

Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low compute cost. However, such hybrid systems suffer from loss metric mismatch when the DNN and HMM are trained independently. Sequence discriminative training cannot fully mitigate the loss-metric mismatch due to the inherent Markovian style of the operation. We propose an low footprint CNN model, called HEiMDaL, to detect and localize keywords in streaming conditions. We introduce an alignment-based classification loss to detect the occurrence of the keyword along with an offset loss to predict the start of the keyword. HEiMDaL shows 73% reduction in detection metrics along with equivalent localization accuracy and with the same memory footprint as existing DNN-HMM style models for a given wake-word.