# EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge

arXiv: https://arxiv.org/abs/1810.08559 · HTML: https://mlsanity.com/paper/1810.08559/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

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

- [SubSpectral Normalization for Neural Audio Data Processing](https://mlsanity.com/paper/2103.13620/) (2021)
- [Federated Action Recognition on Heterogeneous Embedded Devices](https://mlsanity.com/paper/2107.12147/) (2021)
- [Zone-based Federated Learning for Mobile Sensing Data](https://mlsanity.com/paper/2303.06246/) (2023)
- [Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices](https://mlsanity.com/paper/2103.17142/) (2021)
- [Improving the Performance of DNN-based Software Services using Automated Layer Caching](https://mlsanity.com/paper/2209.08625/) (2022)

## Abstract (arXiv)

Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devices. Recently, there have been greater efforts in the design of small, low-footprint deep neural networks (DNNs) that are more appropriate for edge devices, with much of the focus on design principles for hand-crafting efficient network architectures. In this study, we explore a human-machine collaborative design strategy for building low-footprint DNN architectures for speech recognition through a marriage of human-driven principled network design prototyping and machine-driven design exploration. The efficacy of this design strategy is demonstrated through the design of a family of highly-efficient DNNs (nicknamed EdgeSpeechNets) for limited-vocabulary speech recognition. Experimental results using the Google Speech Commands dataset for limited-vocabulary speech recognition showed that EdgeSpeechNets have higher accuracies than state-of-the-art DNNs (with the best EdgeSpeechNet achieving ~97% accuracy), while achieving significantly smaller network sizes (as much as 7.8x smaller) and lower computational cost (as much as 36x fewer multiply-add operations, 10x lower prediction latency, and 16x smaller memory footprint on a Motorola Moto E phone), making them very well-suited for on-device edge voice interface applications.