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 201–250 of 407 papers

TitleStatusHype
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
Wakeword Detection under Distribution Shifts—0
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets—0
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource DevicesCode0
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting—0
Personalized Keyword Spotting through Multi-task Learning—0
Challenges and Opportunities in Multi-device Speech Processing—0
Open-source FPGA-ML codesign for the MLPerf Tiny BenchmarkCode0
QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Avoid Overfitting User Specific Information in Federated Keyword SpottingCode0
Latency Control for Keyword Spotting—0
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning—0
Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs—0
Speech Augmentation Based Unsupervised Learning for Keyword Spotting—0
Boosting Tail Neural Network for Realtime Custom Keyword Spotting—0
A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization—0
Efficient dynamic filter for robust and low computational feature extraction—0
PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting—0
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning—0
Depth Pruning with Auxiliary Networks for TinyMLCode0
AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy—0
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training—0
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting—0
Learning Decoupling Features Through Orthogonality Regularization—0
Filler Word Detection and Classification: A Dataset and BenchmarkCode0
Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention—0
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
Keyword localisation in untranscribed speech using visually grounded speech modelsCode0
Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflowsCode0
ImportantAug: a data augmentation agent for speechCode0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
Phone Based Keyword Spotting for Transcribing Very Low Resource Languages—0
Deep Spoken Keyword Spotting: An Overview—0
Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection—0
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator—0
Ultra-Low Power Keyword Spotting at the Edge—0
WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting—0
Temporal Knowledge Distillation for On-device Audio Classification—0
End-to-end Keyword Spotting using Xception-1dCode0
PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition—0
Multi-task Voice Activated Framework using Self-supervised Learning—0
Speech-MLP: a simple MLP architecture for speech processing—0
Towards Robust Domain Generalization in 2D Neural Audio Processing—0
A Lightweight dynamic filter for keyword spotting—0
Audiomer: A Convolutional Transformer For Keyword SpottingCode0
Behavior of Keyword Spotting Networks Under Noisy Conditions—0
Keyword spotting for audiovisual archival search in Uralic languages—0
Feature learning for efficient ASR-free keyword spotting in low-resource languages—0
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting—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