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 1–25 of 407 papers

TitleStatusHype
Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
ASAP-FE: Energy-Efficient Feature Extraction Enabling Multi-Channel Keyword Spotting on Edge Processors—0
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
GLAP: General contrastive audio-text pretraining across domains and languagesCode2
Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU—0
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models—0
Assessing the Impact of Anisotropy in Neural Representations of Speech: A Case Study on Keyword Spotting—0
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing—0
Speech Unlearning—0
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential DataCode1
LLM-Synth4KWS: Scalable Automatic Generation and Synthesis of Confusable Data for Custom Keyword Spotting—0
MFA-KWS: Effective Keyword Spotting with Multi-head Frame-asynchronous DecodingCode2
Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting—0
GraphemeAug: A Systematic Approach to Synthesized Hard Negative Keyword Spotting Examples—0
AdaKWS: Towards Robust Keyword Spotting with Test-Time Adaptation—0
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting—0
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning—0
Efficient Continual Learning in Keyword Spotting using Binary Neural Networks—0
Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs—0
AI-Powered Agile Analog Circuit Design and Optimization—0
Towards efficient keyword spotting using spike-based time difference encoders—0
Eventprop training for efficient neuromorphic applications—0
Toward noise-robust whisper keyword spotting on headphones with in-earcup microphone and curriculum learning—0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
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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