SOTAVerified

Keyword Spotting

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

( Image credit: Simon Grest )

Papers

Showing 126150 of 407 papers

TitleStatusHype
How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?0
Unsupervised Speech Representation Pooling Using Vector QuantizationCode0
To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement0
AraSpot: Arabic Spoken Command SpottingCode0
Exploring Representation Learning for Small-Footprint Keyword Spotting0
Self-supervised speech representation learning for keyword-spotting with light-weight transformers0
ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents0
Fixed-point quantization aware training for on-device keyword-spotting0
Scalable Weight Reparametrization for Efficient Transfer Learning0
Locale Encoding For Scalable Multilingual Keyword Spotting Models0
Speech Privacy Leakage from Shared Gradients in Distributed Learning0
LipLearner: Customizable Silent Speech Interactions on Mobile DevicesCode1
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons0
Analyzing the Representational Geometry of Acoustic Word Embeddings0
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion0
Learnable Front Ends Based on Temporal Modulation for Music Tagging0
ASiT: Local-Global Audio Spectrogram vIsion Transformer for Event ClassificationCode1
Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting0
PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking0
BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network PerformanceCode1
Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting0
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting0
Integrated Parameter-Efficient Tuning for General-Purpose Audio ModelsCode0
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach0
MAST: Multiscale Audio Spectrogram TransformersCode1
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Benchmark Results

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