# End-to-End User-Defined Keyword Spotting using Shifted Delta Coefficients

arXiv: https://arxiv.org/abs/2405.14489 · HTML: https://mlsanity.com/paper/2405.14489/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 13 of 13)

- [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)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting](https://mlsanity.com/paper/2206.15400/) (2022)
- [End-to-End ASR-free Keyword Search from Speech](https://mlsanity.com/paper/1701.04313/) (2017)
- [Query-by-example on-device keyword spotting](https://mlsanity.com/paper/1910.05171/) (2019)
- [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)
- [Open vocabulary keyword spotting through transfer learning from speech synthesis](https://mlsanity.com/paper/2404.03914/) (2024)

## Built on by (most cited first, 1 of 1)

- [SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting](https://mlsanity.com/paper/2409.09067/) (2024)

## Abstract (arXiv)

Identifying user-defined keywords is crucial for personalizing interactions with smart devices. Previous approaches of user-defined keyword spotting (UDKWS) have relied on short-term spectral features such as mel frequency cepstral coefficients (MFCC) to detect the spoken keyword. However, these features may face challenges in accurately identifying closely related pronunciation of audio-text pairs, due to their limited capability in capturing the temporal dynamics of the speech signal. To address this challenge, we propose to use shifted delta coefficients (SDC) which help in capturing pronunciation variability (transition between connecting phonemes) by incorporating long-term temporal information. The performance of the SDC feature is compared with various baseline features across four different datasets using a cross-attention based end-to-end system. Additionally, various configurations of SDC are explored to find the suitable temporal context for the UDKWS task. The experimental results reveal that the SDC feature outperforms the MFCC baseline feature, exhibiting an improvement of 8.32% in area under the curve (AUC) and 8.69% in terms of equal error rate (EER) on the challenging Libriphrase-hard dataset. Moreover, the proposed approach demonstrated superior performance when compared to state-of-the-art UDKWS techniques.