SOTAVerified

Keyword Spotting

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

( Image credit: Simon Grest )

Papers

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Showing 101–125 of 407 papers

TitleStatusHype
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting—0
Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting—0
Convexity-based Pruning of Speech Representation Models—0
Neuromorphic Keyword Spotting with Pulse Density Modulation MEMS MicrophonesCode0
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting—0
Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
The Role of Temporal Hierarchy in Spiking Neural Networks—0
Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments—0
KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer—0
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical—0
Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural NetworksCode0
Multitaper mel-spectrograms for keyword spotting—0
Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning—0
Micro-power spoken keyword spotting on Xylo Audio 2—0
DASB -- Discrete Audio and Speech Benchmark—0
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting—0
CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting—0
Relational Proxy Loss for Audio-Text based Keyword Spotting—0
RepCNN: Micro-sized, Mighty Models for Wakeword Detection—0
Keyword-Guided Adaptation of Automatic Speech Recognition—0
TinySV: Speaker Verification in TinyML with On-device Learning—0
End-to-End User-Defined Keyword Spotting using Shifted Delta Coefficients—0
Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting—0
DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM—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