About
In speech processing, keyword spotting deals with the identification of keywords in utterances.
( Image credit: Simon Grest )
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Efficient keyword spotting using dilated convolutions and gating
• sonos/tract
• 
We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states.
Hello Edge: Keyword Spotting on Microcontrollers
• ARM-software/ML-KWS-for-MCU
• 
We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements.
Ranked #6 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V1 12 metric)
Deep Residual Learning for Small-Footprint Keyword Spotting
• castorini/honk
•
We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark.
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
• castorini/honk
•
We describe Honk, an open-source PyTorch reimplementation of convolutional neural networks for keyword spotting that are included as examples in TensorFlow.
Low-Power Audio Keyword Spotting using Tsetlin Machines
In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs.
KEYWORD SPOTTING AUDIO AND SPEECH PROCESSING SOUND
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level.
An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
• mindorii/kws
•
We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection.
Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
In addition, we release the implementation of the proposed and the baseline models including an end-to-end pipeline for training models and evaluating them on mobile devices.
Ranked #7 on
Keyword Spotting
on Google Speech Commands
Stochastic Adaptive Neural Architecture Search for Keyword Spotting
• TomVeniat/SANAS
•
The problem of keyword spotting i. e. identifying keywords in a real-time audio stream is mainly solved by applying a neural network over successive sliding windows.
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
• TomVeniat/SANAS
•
Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems.
Ranked #1 on
Keyword Spotting
on TensorFlow