# ProKWS: Personalized Keyword Spotting via Collaborative Learning of Phonemes and Prosody

arXiv: https://arxiv.org/abs/2603.18024 · HTML: https://mlsanity.com/paper/2603.18024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (2024)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings](https://mlsanity.com/paper/1706.03818/) (2017)
- [Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting](https://mlsanity.com/paper/2206.15400/) (2022)
- [DONUT: CTC-based Query-by-Example Keyword Spotting](https://mlsanity.com/paper/1811.10736/) (2018)
- [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)
- [Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding](https://mlsanity.com/paper/2308.06472/) (2023)
- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [Phoneme-Level Contrastive Learning for User-Defined Keyword Spotting with Flexible Enrollment](https://mlsanity.com/paper/2412.20805/) (2024)
- [MM-KWS: Multi-modal Prompts for Multilingual User-defined Keyword Spotting](https://mlsanity.com/paper/2406.07310/) (2024)
- [Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech](https://mlsanity.com/paper/2401.06485/) (2024)

## Abstract (arXiv)

Current keyword spotting systems primarily use phoneme-level matching to distinguish confusable words but ignore user-specific pronunciation traits like prosody (intonation, stress, rhythm). This paper presents ProKWS, a novel framework integrating fine-grained phoneme learning with personalized prosody modeling. We design a dual-stream encoder where one stream derives robust phonemic representations through contrastive learning, while the other extracts speaker-specific prosodic patterns. A collaborative fusion module dynamically combines phonemic and prosodic information, enhancing adaptability across acoustic environments. Experiments show ProKWS delivers highly competitive performance, comparable to state-of-the-art models on standard benchmarks and demonstrates strong robustness for personalized keywords with tone and intent variations.