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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 251–275 of 407 papers

TitleStatusHype
Keyword Transformer: A Self-Attention Model for Keyword SpottingCode1
Auto-KWS 2021 Challenge: Task, Datasets, and BaselinesCode1
Prototype-based Personalized Pruning—0
SubSpectral Normalization for Neural Audio Data Processing—0
EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting—0
An Ultra-low Power RNN Classifier for Always-On Voice Wake-Up Detection Robust to Real-World Scenarios—0
The NPU System for the 2020 Personalized Voice Trigger Challenge—0
Meta-Learning for improving rare word recognition in end-to-end ASR—0
Dynamic curriculum learning via data parameters for noise robust keyword spotting—0
Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss—0
Modular approach to data preprocessing in ALOHA and application to a smart industry use case—0
Speech Enhancement for Wake-Up-Word detection in Voice Assistants—0
Learning Efficient Representations for Keyword Spotting with Triplet LossCode1
Neural Networks for Keyword Spotting on IoT Devices—0
A 510-nW Wake-Up Keyword-Spotting Chip Using Serial-FFT-Based MFCC and Binarized Depthwise Separable CNN in 28-nm CMOS—0
EfficientNet-Absolute Zero for Continuous Speech Keyword SpottingCode1
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and QuantizationCode0
Training Wake Word Detection with Synthesized Speech Data on Confusion Words—0
Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric—0
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
RNNAccel: A Fusion Recurrent Neural Network Accelerator for Edge Intelligence—0
Deep Convolutional Spiking Neural Networks for Keyword Spotting—0
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Small-Footprint Keyword Spotting with Multi-Scale Temporal ConvolutionCode1
Low-Power Low-Latency Keyword Spotting and Adaptive Control with a SpiNNaker 2 Prototype and Comparison with Loihi—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