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 101–150 of 407 papers

TitleStatusHype
Federated Learning for Keyword SpottingCode0
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource DevicesCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoringCode0
JavaScript Convolutional Neural Networks for Keyword Spotting in the Browser: An Experimental AnalysisCode0
Integrated Parameter-Efficient Tuning for General-Purpose Audio ModelsCode0
Keyword localisation in untranscribed speech using visually grounded speech modelsCode0
Boosting keyword spotting through on-device learnable user speech characteristicsCode0
Indian EmoSpeech Command Dataset: A dataset for emotion based speech recognition in the wildCode0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword SpottingCode0
GTM-UVigo Systems for the Query-by-Example Search on Speech Task at MediaEval 2015Code0
Hello Edge: Keyword Spotting on MicrocontrollersCode0
Honkling: In-Browser Personalization for Ubiquitous Keyword SpottingCode0
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the InputCode0
End-to-end Keyword Spotting using Neural Architecture Search and Quantization—0
Behavior of Keyword Spotting Networks Under Noisy Conditions—0
An Alternative Deep Feature Approach to Line Level Keyword Spotting—0
Employing Phonetic Speech Recognition for Language and Dialect Specific Search—0
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor Attacks on Deep Speech Classification Models—0
ELiRF at MediaEval 2015: Query by Example Search on Speech Task (QUESST)—0
ELiRF at MediaEval 2014: Query by Example Search on Speech Task (QUESST)—0
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator—0
Efficient keyword spotting using time delay neural networks—0
Automating speech reception threshold measurements using automatic speech recognition—0
Automatic Speech Recognition for Humanitarian Applications in Somali—0
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection—0
Efficient dynamic filter for robust and low computational feature extraction—0
Efficient Continual Learning in Keyword Spotting using Binary Neural Networks—0
Automatic Extraction of News Values from Headline Text—0
Effective Integration of KAN for Keyword Spotting—0
Effective Combination of DenseNet andBiLSTM for Keyword Spotting—0
AutoKWS: Keyword Spotting with Differentiable Architecture Search—0
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting—0
Adaptive Speech Understanding for Intuitive Model-based Spoken Dialogues—0
AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy—0
EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting—0
Dynamic curriculum learning via data parameters for noise robust keyword spotting—0
Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device—0
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting—0
DONUT: CTC-based Query-by-Example Keyword Spotting—0
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study—0
AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data—0
A Literature Review of Keyword Spotting Technologies for Urdu—0
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning—0
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting—0
Discriminatory and orthogonal feature learning for noise robust keyword spotting—0
Developing Far-Field Speaker System Via Teacher-Student Learning—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