SOTAVerified

Keyword Spotting

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

( Image credit: Simon Grest )

Papers

Showing 301–325 of 407 papers

TitleStatusHype
Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling—0
Performance-Oriented Neural Architecture Search—0
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection—0
Predicting detection filters for small footprint open-vocabulary keyword spotting—0
Small-footprint Keyword Spotting with Graph Convolutional Network—0
Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-ConvolutionsCode0
Honkling: In-Browser Personalization for Ubiquitous Keyword SpottingCode0
Temporal Feedback Convolutional Recurrent Neural Networks for Speech Command RecognitionCode0
Adversarial Example Detection by Classification for Deep Speech RecognitionCode0
Indian EmoSpeech Command Dataset: A dataset for emotion based speech recognition in the wildCode0
Query-by-example on-device keyword spotting—0
Orthogonality Constrained Multi-Head Attention For Keyword Spotting—0
Spoken Language Identification using ConvNets—0
Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data—0
A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting—0
Multi-layer Attention Mechanism for Speech Keyword Recognition—0
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation—0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training—0
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers—0
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting—0
An Alternative Deep Feature Approach to Line Level Keyword Spotting—0
SpeechYOLO: Detection and Localization of Speech Objects—0
Temporal Convolution for Real-time Keyword Spotting on Mobile DevicesCode0
On evaluating CNN representations for low resource medical image classification—0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
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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