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 151–200 of 407 papers

TitleStatusHype
A Joint Model of Orthography and Morphological Segmentation—0
Learning Decoupling Features Through Orthogonality Regularization—0
Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention—0
A Streaming End-to-End Framework For Spoken Language Understanding—0
KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer—0
Deep Spoken Keyword Spotting: An Overview—0
AI-Powered Agile Analog Circuit Design and Optimization—0
AdaKWS: Towards Robust Keyword Spotting with Test-Time Adaptation—0
DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM—0
Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU—0
Deep Convolutional Spiking Neural Networks for Keyword Spotting—0
Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data—0
Assessing the Impact of Anisotropy in Neural Representations of Speech: A Case Study on Keyword Spotting—0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting—0
Data Augmentation for Robust Keyword Spotting under Playback Interference—0
ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages—0
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools—0
DASB -- Discrete Audio and Speech Benchmark—0
Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder—0
Dark Experience for Incremental Keyword Spotting—0
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition—0
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning—0
CUNY Systems for the Query-by-Example Search on Speech Task at MediaEval 2015—0
ASAP-FE: Energy-Efficient Feature Extraction Enabling Multi-Channel Keyword Spotting on Edge Processors—0
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons—0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training—0
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation—0
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data—0
CUHK System for QUESST Task of MediaEval 2014—0
Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection—0
台語關鍵詞辨識之實作與比較 (Implementation and Comparison of Keyword Spotting for Taiwanese) [In Chinese]—0
CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting—0
iPhonMatchNet: Zero-Shot User-Defined Keyword Spotting Using Implicit Acoustic Echo Cancellation—0
IIIT-H System for MediaEval 2014 QUESST—0
Keyword-Guided Adaptation of Automatic Speech Recognition—0
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting—0
How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?—0
Keyword spotting -- Detecting commands in speech using deep learning—0
Keyword spotting for audiovisual archival search in Uralic languages—0
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers—0
Convexity-based Pruning of Speech Representation Models—0
A Probabilistic Framework for Lexicon-based Keyword Spotting in Handwritten Text Images—0
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts—0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
Latency Control for Keyword Spotting—0
Learnable Front Ends Based on Temporal Modulation for Music Tagging—0
Hierarchical Neural Network Architecture In Keyword Spotting—0
Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology—0
Application of Knowledge Distillation to Multi-task Speech Representation Learning—0
HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words—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