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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 371–380 of 407 papers

Title	Date	Tasks	Status	Hype	Score
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting	Aug 23, 2024	Keyword SpottingSmall-Footprint Keyword Spotting	—Unverified	0	0
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study	Sep 27, 2023	Automatic Speech RecognitionKeyword Spotting	—Unverified	0	0
DONUT: CTC-based Query-by-Example Keyword Spotting	Oct 22, 2018	Keyword Spotting	—Unverified	0	0
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting	Jun 28, 2022	Keyword SpottingMetric Learning	—Unverified	0	0
Dynamic curriculum learning via data parameters for noise robust keyword spotting	Feb 18, 2021	Keyword Spotting	—Unverified	0	0
EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting	Mar 14, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0	0
Effective Combination of DenseNet andBiLSTM for Keyword Spotting	Jan 19, 2019	Keyword SpottingTime Series	—Unverified	0	0
Effective Integration of KAN for Keyword Spotting	Sep 13, 2024	Keyword SpottingKolmogorov-Arnold Networks	—Unverified	0	0
Efficient Continual Learning in Keyword Spotting using Binary Neural Networks	May 5, 2025	Continual LearningKeyword Spotting	—Unverified	0	0
Efficient dynamic filter for robust and low computational feature extraction	May 3, 2022	Keyword SpottingSpeaker Verification	—Unverified	0	0

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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