# Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices

arXiv: https://arxiv.org/abs/2508.04857 · HTML: https://mlsanity.com/paper/2508.04857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 54 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2307.06949/) (2023)
- [Learning the Pareto Front with Hypernetworks](https://mlsanity.com/paper/2010.04104/) (2020)
- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1703.05390/) (2017)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [HyperShot: Few-Shot Learning by Kernel HyperNetworks](https://mlsanity.com/paper/2203.11378/) (2022)
- [Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions](https://mlsanity.com/paper/1911.02086/) (2019)
- [Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting](https://mlsanity.com/paper/2206.15400/) (2022)
- [Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models](https://mlsanity.com/paper/1710.09617/) (2017)
- [Attention-based End-to-End Models for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1803.10916/) (2018)
- [DONUT: CTC-based Query-by-Example Keyword Spotting](https://mlsanity.com/paper/1811.10736/) (2018)
- [Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2004.12200/) (2020)
- [Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution](https://mlsanity.com/paper/2010.09960/) (2020)
- [Matching Latent Encoding for Audio-Text based Keyword Spotting](https://mlsanity.com/paper/2306.05245/) (2023)
- [PhonMatchNet: Phoneme-Guided Zero-Shot Keyword Spotting for User-Defined Keywords](https://mlsanity.com/paper/2308.16511/) (2023)
- [Predicting detection filters for small footprint open-vocabulary keyword spotting](https://mlsanity.com/paper/1912.07575/) (2019)
- [Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding](https://mlsanity.com/paper/2308.06472/) (2023)
- [Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks](https://mlsanity.com/paper/2002.10851/) (2020)
- [Personalized Keyphrase Detection using Speaker and Environment Information](https://mlsanity.com/paper/2104.13970/) (2021)
- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [CNN-based Spoken Term Detection and Localization without Dynamic Programming](https://mlsanity.com/paper/2103.05468/) (2021)

## Abstract (arXiv)

Open-vocabulary keyword spotting (KWS) refers to the task of detecting words or terms within speech recordings, regardless of whether they were included in the training data. This paper introduces an open-vocabulary keyword spotting model with state-of-the-art detection accuracy for small-footprint devices. The model is composed of a speech encoder, a target keyword encoder, and a detection network. The speech encoder is either a tiny Whisper or a tiny Conformer. The target keyword encoder is implemented as a hyper-network that takes the desired keyword as a character string and generates a unique set of weights for a convolutional layer, which can be considered as a keyword-specific matched filter. The detection network uses the matched-filter weights to perform a keyword-specific convolution, which guides the cross-attention mechanism of a Perceiver module in determining whether the target term appears in the recording. The results indicate that our system achieves state-of-the-art detection performance and generalizes effectively to out-of-domain conditions, including second-language (L2) speech. Notably, our smallest model, with just 4.2 million parameters, matches or outperforms models that are several times larger, demonstrating both efficiency and robustness.