# NTC-KWS: Noise-aware CTC for Robust Keyword Spotting

arXiv: https://arxiv.org/abs/2412.12614 · HTML: https://mlsanity.com/paper/2412.12614/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [Pmeta-TLA: Backdoor Attacks for Speech Classification Models via Meta-Learning with Timbre Leakage Attack](https://mlsanity.com/paper/2607.01702/) (2026)
- [DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning](https://mlsanity.com/paper/2607.01729/) (2026)
- [A Training Criterion with Token-Level Tolerance to Transcription Ambiguity for Automatic Speech Recognition](https://mlsanity.com/paper/2609.30160/) (2026)
- [Normalise or condition? Noise-floor front-ends for on-board keyword spotting under UAV rotor ego-noise](https://mlsanity.com/paper/2609.31699/) (2026)

## Abstract (arXiv)

In recent years, there has been a growing interest in designing small-footprint yet effective Connectionist Temporal Classification based keyword spotting (CTC-KWS) systems. They are typically deployed on low-resource computing platforms, where limitations on model size and computational capacity create bottlenecks under complicated acoustic scenarios. Such constraints often result in overfitting and confusion between keywords and background noise, leading to high false alarms. To address these issues, we propose a noise-aware CTC-based KWS (NTC-KWS) framework designed to enhance model robustness in noisy environments, particularly under extremely low signal-to-noise ratios. Our approach introduces two additional noise-modeling wildcard arcs into the training and decoding processes based on weighted finite state transducer (WFST) graphs: self-loop arcs to address noise insertion errors and bypass arcs to handle masking and interference caused by excessive noise. Experiments on clean and noisy Hey Snips show that NTC-KWS outperforms state-of-the-art (SOTA) end-to-end systems and CTC-KWS baselines across various acoustic conditions, with particularly strong performance in low SNR scenarios.