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Keyword Spotting

In speech processing, keyword spotting deals with the identification of keywords in utterances.

( Image credit: Simon Grest )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 91–100 of 407 papers

Title	Date	Tasks	Status	Hype	Score
GTM-UVigo Systems for the Query-by-Example Search on Speech Task at MediaEval 2015	Sep 14, 2015	DecoderDynamic Time Warping	CodeCode Available	0	5
Hello Edge: Keyword Spotting on Microcontrollers	Nov 20, 2017	Keyword Spotting	CodeCode Available	0	5
Filler Word Detection and Classification: A Dataset and Benchmark	Mar 28, 2022	ClassificationKeyword Spotting	CodeCode Available	0	5
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting	Oct 18, 2017	Keyword Spottingspeech-recognition	CodeCode Available	0	5
Federated Learning for Keyword Spotting	Oct 9, 2018	Federated LearningKeyword Spotting	CodeCode Available	0	5
Adversarial Example Detection by Classification for Deep Speech Recognition	Oct 22, 2019	ClassificationGeneral Classification	CodeCode Available	0	5
Building and benchmarking an Arabic Speech Commands dataset for small-footprint keyword spotting	May 7, 2021	BenchmarkingDeep Learning	CodeCode Available	0	5
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition	Mar 18, 2019	DecoderHandwritten Text Recognition	CodeCode Available	0	5
Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting	Nov 1, 2019	Keyword Spotting	CodeCode Available	0	5
Efficient keyword spotting using dilated convolutions and gating	Nov 19, 2018	Keyword SpottingSmall-Footprint Keyword Spotting	CodeCode Available	0	5

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All datasets QUESST Google Speech Commands hey Siri FKD Google Speech Commands V2 35 TensorFlow VoxForge Google Speech Commands (v2)Google Speech Commands V2 12

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	NNI non-filtered(for the development set)	Cnxe	6.09	—	Unverified
2	NNI Choi(for the development set)	Cnxe	5.89	—	Unverified
3	NTU rnn (eval)	Cnxe	2.01	—	Unverified
4	NTU dtw (eval)	Cnxe	2.01	—	Unverified
5	NTU dtw (dev)	Cnxe	2.01	—	Unverified
6	NTU rnn (dev)	Cnxe	2.01	—	Unverified
7	ELiRF SDTW (eval)	Cnxe	1.19	—	Unverified
8	ELiRF SDTW-avg (eval)	Cnxe	1.07	—	Unverified
9	ELiRF SDTW (dev)	Cnxe	1.07	—	Unverified
10	CUNY [Subseq+MFCC] (eval)	Cnxe	1.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	WaveFormer	Google Speech Commands V2 12	98.8	—	Unverified
2	QNN	Google Speech Commands V2 35	98.6	—	Unverified
3	TripletLoss-res15	Google Speech Commands V1 12	98.56	—	Unverified
4	M2D	Google Speech Commands V2 35	98.5	—	Unverified
5	EAT-S	Google Speech Commands V2 35	98.15	—	Unverified
6	Audio Spectrogram Transformer	Google Speech Commands V2 35	98.11	—	Unverified
7	EdgeCRNN 2.0×	Google Speech Commands V2 12	98.05	—	Unverified
8	BC-ResNet-8	Google Speech Commands V1 12	98	—	Unverified
9	HTS-AT	Google Speech Commands V2 35	98	—	Unverified
10	Wav2KWS	Google Speech Commands V1 12	97.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Stacked 1D CNN	Error Rate	1.99	—	Unverified
2	End-to-end DNN-HMM	Error Rate	1.7	—	Unverified
3	HEiMDaL	Error Rate	0.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Res26	Accuracy	95.88	—	Unverified
2	EfficientNet-A0 + SA + TL	Accuracy	95.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	QuaternionNeuralNetwork	Accuracy (10-fold)	98.53	—	Unverified
2	SSAMBA	Accuracy (10-fold)	97.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TensorFlow's model version 2	TFMA	89.7	—	Unverified
2	TensorFlow's model version 1	TFMA	85.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	2D-ConvNet	Accuracy (%)	95.4	—	Unverified
2	1D-ConvNet	Accuracy (%)	93.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Quaternion Neural Networks	Accuracy(10-fold)	98.53	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MicroNet-KWS-L	Accuracy	95.3	—	Unverified