# Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning

arXiv: https://arxiv.org/abs/2504.19030 · HTML: https://mlsanity.com/paper/2504.19030/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Survey on Deep Neural Networks in Speech and Vision Systems](https://mlsanity.com/paper/1908.07656/) (2019)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems](https://mlsanity.com/paper/2405.18624/) (2024)
- [Listening to the World Improves Speech Command Recognition](https://mlsanity.com/paper/1710.08377/) (2017)
- [Automatic Speech Recognition with BERT and CTC Transformers: A Review](https://mlsanity.com/paper/2410.09456/) (2024)
- [Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives](https://mlsanity.com/paper/2403.15442/) (2024)

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

- [Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments](https://mlsanity.com/paper/2505.01632/) (2025)

## Abstract (arXiv)

This work addresses the need for enhanced accuracy and efficiency in speech command recognition systems, a critical component for improving user interaction in various smart applications. Leveraging the robust pretrained YAMNet model and transfer learning, this study develops a method that significantly improves speech command recognition. We adapt and train a YAMNet deep learning model to effectively detect and interpret speech commands from audio signals. Using the extensively annotated Speech Commands dataset (speech_commands_v0.01), our approach demonstrates the practical application of transfer learning to accurately recognize a predefined set of speech commands. The dataset is meticulously augmented, and features are strategically extracted to boost model performance. As a result, the final model achieved a recognition accuracy of 95.28%, underscoring the impact of advanced machine learning techniques on speech command recognition. This achievement marks substantial progress in audio processing technologies and establishes a new benchmark for future research in the field.