# Text-Aware Adapter for Few-Shot Keyword Spotting

arXiv: https://arxiv.org/abs/2412.18142 · HTML: https://mlsanity.com/paper/2412.18142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Few-Shot Keyword Spotting in Any Language](https://mlsanity.com/paper/2104.01454/) (2021)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [Multi-view Recurrent Neural Acoustic Word Embeddings](https://mlsanity.com/paper/1611.04496/) (2016)
- [Multilingual Jointly Trained Acoustic and Written Word Embeddings](https://mlsanity.com/paper/2006.14007/) (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)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification](https://mlsanity.com/paper/2303.00204/) (2023)
- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [Metric Learning for User-defined Keyword Spotting](https://mlsanity.com/paper/2211.00439/) (2022)
- [On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting](https://mlsanity.com/paper/2204.00352/) (2022)
- [AdaMS: Deep Metric Learning with Adaptive Margin and Adaptive Scale for Acoustic Word Discrimination](https://mlsanity.com/paper/2210.14564/) (2022)
- [Relational Proxy Loss for Audio-Text based Keyword Spotting](https://mlsanity.com/paper/2406.05314/) (2024)
- [Text-aware Speech Separation for Multi-talker Keyword Spotting](https://mlsanity.com/paper/2406.12447/) (2024)
- [SE/BN Adapter: Parametric Efficient Domain Adaptation for Speaker Recognition](https://mlsanity.com/paper/2406.07832/) (2024)
- [Few-Shot Keyword Spotting from Mixed Speech](https://mlsanity.com/paper/2407.06078/) (2024)

## Built on by (most cited first, 2 of 2)

- [Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting](https://mlsanity.com/paper/2505.16735/) (2025)
- [MATE: Matryoshka Audio-Text Embeddings for Open-Vocabulary Keyword Spotting](https://mlsanity.com/paper/2601.14012/) (2026)

## Abstract (arXiv)

Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room for improvement in target keyword performance. In this work, we propose a novel few-shot transfer learning method, called text-aware adapter (TA-adapter), designed to enhance a pre-trained flexible KWS model for specific keywords with limited speech samples. To adapt the acoustic encoder, we leverage a jointly pre-trained text encoder to generate a text embedding that acts as a representative vector for the keyword. By fine-tuning only a small portion of the network while keeping the core components' weights intact, the TA-adapter proves highly efficient for few-shot KWS, enabling a seamless return to the original pre-trained model. In our experiments, the TA-adapter demonstrated significant performance improvements across 35 distinct keywords from the Google Speech Commands V2 dataset, with only a 0.14% increase in the total number of parameters.