# Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting

arXiv: https://arxiv.org/abs/1703.05390 · HTML: https://mlsanity.com/paper/1703.05390/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 30 of 32)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Learning Efficient Representations for Keyword Spotting with Triplet Loss](https://mlsanity.com/paper/2101.04792/) (2021)
- [Temporal Knowledge Distillation for On-device Audio Classification](https://mlsanity.com/paper/2110.14131/) (2021)
- [Visual Keyword Spotting with Attention](https://mlsanity.com/paper/2110.15957/) (2021)
- [Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review](https://mlsanity.com/paper/2311.11883/) (2023)
- [Synthetic Dataset Generation of Driver Telematics](https://mlsanity.com/paper/2102.00252/) (2021)
- [Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting](https://mlsanity.com/paper/2109.14725/) (2021)
- [Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers](https://mlsanity.com/paper/2208.02765/) (2022)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants](https://mlsanity.com/paper/2109.09958/) (2021)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers](https://mlsanity.com/paper/2509.07051/) (2025)
- [Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset](https://mlsanity.com/paper/2510.21038/) (2025)
- [End-to-end Keyword Spotting using Neural Architecture Search and Quantization](https://mlsanity.com/paper/2104.06666/) (2021)
- [Encoder-Decoder Neural Architecture Optimization for Keyword Spotting](https://mlsanity.com/paper/2106.02738/) (2021)
- [Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting](https://mlsanity.com/paper/2211.06478/) (2022)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (2023)
- [Multi-task Learning with Cross Attention for Keyword Spotting](https://mlsanity.com/paper/2107.07634/) (2021)
- [A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting](https://mlsanity.com/paper/2202.02361/) (2022)
- [Improving Voice Trigger Detection with Metric Learning](https://mlsanity.com/paper/2204.02455/) (2022)
- [Speech Augmentation Based Unsupervised Learning for Keyword Spotting](https://mlsanity.com/paper/2205.14329/) (2022)
- [RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features](https://mlsanity.com/paper/2504.18556/) (2025)
- [Neural Networks for Keyword Spotting on IoT Devices](https://mlsanity.com/paper/2101.00693/) (2021)
- [Real-time low-resource phoneme recognition on edge devices](https://mlsanity.com/paper/2103.13997/) (2021)
- [A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning](https://mlsanity.com/paper/2202.08509/) (2022)
- [Frequency-centroid features for word recognition of non-native English speakers](https://mlsanity.com/paper/2206.07176/) (2022)
- [AraSpot: Arabic Spoken Command Spotting](https://mlsanity.com/paper/2303.16621/) (2023)
- [Hijacking Attacks against Neural Networks by Analyzing Training Data](https://mlsanity.com/paper/2401.09740/) (2024)
- [Temporal assessment of malicious behaviors: application to turnout field data monitoring](https://mlsanity.com/paper/2405.02346/) (2024)
- [Multitaper mel-spectrograms for keyword spotting](https://mlsanity.com/paper/2407.04662/) (2024)

## Abstract (arXiv)

Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-of-the-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range context. We analyze the effect of architecture parameters, and propose training strategies to improve performance. With only ~230k parameters, our CRNN model yields acceptably low latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise ratio.