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 251–300 of 407 papers

TitleStatusHype
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting—0
Text-Aware Adapter for Few-Shot Keyword Spotting—0
The DKU System Description for The Interspeech 2021 Auto-KWS Challenge—0
The Effects of Data Collection Methods in Twitter—0
The IIT-B Query-by-Example System for MediaEval 2015—0
The NNI Query-by-Example System for MediaEval 2014—0
The NNI Query-by-Example System for MediaEval 2015—0
The NPU System for the 2020 Personalized Voice Trigger Challenge—0
The RATS Collection: Supporting HLT Research with Degraded Audio Data—0
The Role of Temporal Hierarchy in Spiking Neural Networks—0
The SPL-IT Query by Example Search on Speech system for MediaEval 2014—0
The SPL-IT-UC Query by Example Search on Speech system for MediaEval 2015—0
TinySV: Speaker Verification in TinyML with On-device Learning—0
To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement—0
Toward noise-robust whisper keyword spotting on headphones with in-earcup microphone and curriculum learning—0
Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting—0
Towards efficient keyword spotting using spike-based time difference encoders—0
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili—0
Towards Robust Domain Generalization in 2D Neural Audio Processing—0
Training Keyword Spotting Models on Non-IID Data with Federated Learning—0
Training Wake Word Detection with Synthesized Speech Data on Confusion Words—0
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources—0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
TUKE at MediaEval 2015 QUESST—0
TUKE System for MediaEval 2014 QUESST—0
U2-KWS: Unified Two-pass Open-vocabulary Keyword Spotting with Keyword Bias—0
Ultra-Low Power Keyword Spotting at the Edge—0
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
Visually grounded cross-lingual keyword spotting in speech—0
Vocal Tract Length Warped Features for Spoken Keyword Spotting—0
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion—0
Wakeword Detection under Distribution Shifts—0
WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting—0
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing—0
Weight-importance sparse training in keyword spotting—0
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding—0
Work in Progress: Linear Transformers for TinyML—0
WSRNet: Joint Spotting and Recognition of Handwritten Words—0
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach—0
Zero-Shot Federated Learning with New Classes for Audio Classification—0
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Zone-based Keyword Spotting in Bangla and Devanagari Documents—0
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models—0
Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU—0
A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization—0
A 510-nW Wake-Up Keyword-Spotting Chip Using Serial-FFT-Based MFCC and Binarized Depthwise Separable CNN in 28-nm CMOS—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