# Keyword spotting -- Detecting commands in speech using deep learning

arXiv: https://arxiv.org/abs/2312.05640 · HTML: https://mlsanity.com/paper/2312.05640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 9 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [A neural attention model for speech command recognition](https://mlsanity.com/paper/1808.08929/) (2018)

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

- [Polariton lattices as binarized neuromorphic networks](https://mlsanity.com/paper/2401.07232/) (2024)

## Abstract (arXiv)

Speech recognition has become an important task in the development of machine learning and artificial intelligence. In this study, we explore the important task of keyword spotting using speech recognition machine learning and deep learning techniques. We implement feature engineering by converting raw waveforms to Mel Frequency Cepstral Coefficients (MFCCs), which we use as inputs to our models. We experiment with several different algorithms such as Hidden Markov Model with Gaussian Mixture, Convolutional Neural Networks and variants of Recurrent Neural Networks including Long Short-Term Memory and the Attention mechanism. In our experiments, RNN with BiLSTM and Attention achieves the best performance with an accuracy of 93.9 %