SOTAVerified

Keyword Spotting

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

( Image credit: Simon Grest )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 407 papers

TitleStatusHype
A Joint Model of Orthography and Morphological Segmentation—0
Online Keyword Spotting with a Character-Level Recurrent Neural Network—0
Structured Transforms for Small-Footprint Deep Learning—0
CUNY Systems for the Query-by-Example Search on Speech Task at MediaEval 2015—0
The SPL-IT-UC Query by Example Search on Speech system for MediaEval 2015—0
ELiRF at MediaEval 2015: Query by Example Search on Speech Task (QUESST)—0
The NNI Query-by-Example System for MediaEval 2015—0
NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection Using Deep and Recurrent Neural Networks—0
The IIT-B Query-by-Example System for MediaEval 2015—0
TUKE at MediaEval 2015 QUESST—0
GTM-UVigo Systems for the Query-by-Example Search on Speech Task at MediaEval 2015Code0
BUT QUESST 2015 System Description—0
Speech and language technologies for the automatic monitoring and training of cognitive functions—0
Finding Opinion Manipulation Trolls in News Community Forums—0
What’s Cookin’? Interpreting Cooking Videos using Text, Speech and VisionCode0
What's Cookin'? Interpreting Cooking Videos using Text, Speech and VisionCode0
The NNI Query-by-Example System for MediaEval 2014—0
IIIT-H System for MediaEval 2014 QUESST—0
BUT QUESST 2014 System Description—0
ELiRF at MediaEval 2014: Query by Example Search on Speech Task (QUESST)—0
The SPL-IT Query by Example Search on Speech system for MediaEval 2014—0
GTTS-EHU Systems for QUESST at MediaEval 2014—0
TUKE System for MediaEval 2014 QUESST—0
CUHK System for QUESST Task of MediaEval 2014—0
Morphological Segmentation for Keyword Spotting—0
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All datasetsQUESSTGoogle Speech Commandshey SiriFKDGoogle Speech Commands V2 35TensorFlowVoxForgeGoogle Speech Commands (v2)Google Speech Commands V2 12

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NNI non-filtered(for the development set)Cnxe6.09—Unverified
2NNI Choi(for the development set)Cnxe5.89—Unverified
3NTU rnn (eval)Cnxe2.01—Unverified
4NTU dtw (eval)Cnxe2.01—Unverified
5NTU dtw (dev)Cnxe2.01—Unverified
6NTU rnn (dev)Cnxe2.01—Unverified
7ELiRF SDTW (eval)Cnxe1.19—Unverified
8ELiRF SDTW-avg (eval)Cnxe1.07—Unverified
9ELiRF SDTW (dev)Cnxe1.07—Unverified
10CUNY [Subseq+MFCC] (eval)Cnxe1.07—Unverified
#ModelMetricClaimedVerifiedStatus
1WaveFormerGoogle Speech Commands V2 1298.8—Unverified
2QNNGoogle Speech Commands V2 3598.6—Unverified
3TripletLoss-res15Google Speech Commands V1 1298.56—Unverified
4M2DGoogle Speech Commands V2 3598.5—Unverified
5EAT-SGoogle Speech Commands V2 3598.15—Unverified
6Audio Spectrogram TransformerGoogle Speech Commands V2 3598.11—Unverified
7EdgeCRNN 2.0×Google Speech Commands V2 1298.05—Unverified
8BC-ResNet-8Google Speech Commands V1 1298—Unverified
9HTS-ATGoogle Speech Commands V2 3598—Unverified
10Wav2KWSGoogle Speech Commands V1 1297.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Stacked 1D CNNError Rate1.99—Unverified
2End-to-end DNN-HMMError Rate1.7—Unverified
3HEiMDaLError Rate0.45—Unverified
#ModelMetricClaimedVerifiedStatus
1Res26Accuracy95.88—Unverified
2EfficientNet-A0 + SA + TLAccuracy95.83—Unverified
#ModelMetricClaimedVerifiedStatus
1QuaternionNeuralNetworkAccuracy (10-fold)98.53—Unverified
2SSAMBAAccuracy (10-fold)97.4—Unverified
#ModelMetricClaimedVerifiedStatus
1TensorFlow's model version 2TFMA89.7—Unverified
2TensorFlow's model version 1TFMA85.4—Unverified
#ModelMetricClaimedVerifiedStatus
12D-ConvNetAccuracy (%)95.4—Unverified
21D-ConvNetAccuracy (%)93.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Quaternion Neural NetworksAccuracy(10-fold)98.53—Unverified
#ModelMetricClaimedVerifiedStatus
1MicroNet-KWS-LAccuracy95.3—Unverified