# CNN-based Spoken Term Detection and Localization without Dynamic Programming

arXiv: https://arxiv.org/abs/2103.05468 · HTML: https://mlsanity.com/paper/2103.05468/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Zero-Shot Object Detection by Hybrid Region Embedding](https://mlsanity.com/paper/1805.06157/) (2018)
- [Deep convolutional acoustic word embeddings using word-pair side information](https://mlsanity.com/paper/1510.01032/) (2015)
- [Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches](https://mlsanity.com/paper/1611.02550/) (2016)
- [Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings](https://mlsanity.com/paper/1706.03818/) (2017)
- [Multi-view Recurrent Neural Acoustic Word Embeddings](https://mlsanity.com/paper/1611.04496/) (2016)
- [SpeechYOLO: Detection and Localization of Speech Objects](https://mlsanity.com/paper/1904.07704/) (2019)
- [Multilingual Bottleneck Features for Query by Example Spoken Term Detection](https://mlsanity.com/paper/1907.00443/) (2019)

## Built on by (most cited first, 6 of 6)

- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units](https://mlsanity.com/paper/2407.04652/) (2024)
- [End-to-End Open Vocabulary Keyword Search With Multilingual Neural Representations](https://mlsanity.com/paper/2308.08027/) (2023)
- [Written Term Detection Improves Spoken Term Detection](https://mlsanity.com/paper/2407.04601/) (2024)
- [PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting](https://mlsanity.com/paper/2508.03190/) (2025)
- [Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices](https://mlsanity.com/paper/2508.04857/) (2025)

## Abstract (arXiv)

In this paper, we propose a spoken term detection algorithm for simultaneous prediction and localization of in-vocabulary and out-of-vocabulary terms within an audio segment. The proposed algorithm infers whether a term was uttered within a given speech signal or not by predicting the word embeddings of various parts of the speech signal and comparing them to the word embedding of the desired term. The algorithm utilizes an existing embedding space for this task and does not need to train a task-specific embedding space. At inference the algorithm simultaneously predicts all possible locations of the target term and does not need dynamic programming for optimal search. We evaluate our system on several spoken term detection tasks on read speech corpora.