# Streaming keyword spotting on mobile devices

arXiv: https://arxiv.org/abs/2005.06720 · HTML: https://mlsanity.com/paper/2005.06720/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 30 of 47)

- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://mlsanity.com/paper/2104.00769/) (2021)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [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)
- [StreamVC: Real-Time Low-Latency Voice Conversion](https://mlsanity.com/paper/2401.03078/) (2024)
- [The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language](https://mlsanity.com/paper/2311.08323/) (2023)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems](https://mlsanity.com/paper/2306.02161/) (2023)
- [Streamable Neural Audio Synthesis With Non-Causal Convolutions](https://mlsanity.com/paper/2204.07064/) (2022)
- [Personalized Keyword Spotting through Multi-task Learning](https://mlsanity.com/paper/2206.13708/) (2022)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [EgoTrigger: Toward Audio-Driven Image Capture for Human Memory Enhancement in All-Day Energy-Efficient Smart Glasses](https://mlsanity.com/paper/2508.01915/) (2025)
- [A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate](https://mlsanity.com/paper/2108.04051/) (2021)
- [Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting](https://mlsanity.com/paper/2109.14725/) (2021)
- [Audiomer: A Convolutional Transformer For Keyword Spotting](https://mlsanity.com/paper/2109.10252/) (2021)
- [Avoid Overfitting User Specific Information in Federated Keyword Spotting](https://mlsanity.com/paper/2206.08864/) (2022)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance](https://mlsanity.com/paper/2211.06987/) (2022)
- [SpectroStream: A Versatile Neural Codec for General Audio](https://mlsanity.com/paper/2508.05207/) (2025)
- [TB or not TB? Acoustic cough analysis for tuberculosis classification](https://mlsanity.com/paper/2209.00934/) (2022)
- [Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data](https://mlsanity.com/paper/2505.24852/) (2025)
- [Encoder-Decoder Neural Architecture Optimization for Keyword Spotting](https://mlsanity.com/paper/2106.02738/) (2021)
- [Attention-Free Keyword Spotting](https://mlsanity.com/paper/2110.07749/) (2021)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (2023)
- [Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments](https://mlsanity.com/paper/2407.16840/) (2024)
- [GE2E-KWS: Generalized End-to-End Training and Evaluation for Zero-shot Keyword Spotting](https://mlsanity.com/paper/2410.16647/) (2024)
- [A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting](https://mlsanity.com/paper/2202.02361/) (2022)
- [I see what you hear: a vision-inspired method to localize words](https://mlsanity.com/paper/2210.13567/) (2022)

## Abstract (arXiv)

In this work we explore the latency and accuracy of keyword spotting (KWS) models in streaming and non-streaming modes on mobile phones. NN model conversion from non-streaming mode (model receives the whole input sequence and then returns the classification result) to streaming mode (model receives portion of the input sequence and classifies it incrementally) may require manual model rewriting. We address this by designing a Tensorflow/Keras based library which allows automatic conversion of non-streaming models to streaming ones with minimum effort. With this library we benchmark multiple KWS models in both streaming and non-streaming modes on mobile phones and demonstrate different tradeoffs between latency and accuracy. We also explore novel KWS models with multi-head attention which reduce the classification error over the state-of-art by 10% on Google speech commands data sets V2. The streaming library with all experiments is open-sourced.