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
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting—0
Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting—0
Convexity-based Pruning of Speech Representation Models—0
Neuromorphic Keyword Spotting with Pulse Density Modulation MEMS MicrophonesCode0
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting—0
Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
The Role of Temporal Hierarchy in Spiking Neural Networks—0
Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments—0
KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer—0
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical—0
Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural NetworksCode0
Multitaper mel-spectrograms for keyword spotting—0
Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning—0
Micro-power spoken keyword spotting on Xylo Audio 2—0
DASB -- Discrete Audio and Speech Benchmark—0
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting—0
CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting—0
Relational Proxy Loss for Audio-Text based Keyword Spotting—0
Keyword-Guided Adaptation of Automatic Speech Recognition—0
RepCNN: Micro-sized, Mighty Models for Wakeword Detection—0
TinySV: Speaker Verification in TinyML with On-device Learning—0
End-to-End User-Defined Keyword Spotting using Shifted Delta Coefficients—0
Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting—0
DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM—0
Multi-Sample Dynamic Time Warping for Few-Shot Keyword Spotting—0
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy ConditionsCode0
Noise-Robust Keyword Spotting through Self-supervised PretrainingCode0
Work in Progress: Linear Transformers for TinyML—0
More than words: Advancements and challenges in speech recognition for singing—0
On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems—0
Boosting keyword spotting through on-device learnable user speech characteristicsCode0
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
Multilingual acoustic word embeddings for zero-resource languages—0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting—0
U2-KWS: Unified Two-pass Open-vocabulary Keyword Spotting with Keyword Bias—0
Keyword spotting -- Detecting commands in speech using deep learning—0
Personalizing Keyword Spotting with Speaker Information—0
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correctionCode0
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study—0
On the Non-Associativity of Analog Computations—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
Cluster-based pruning techniques for audio dataCode0
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks—0
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting—0
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization—0
iPhonMatchNet: Zero-Shot User-Defined Keyword Spotting Using Implicit Acoustic Echo Cancellation—0
Leveraging Large Language Models for Exploiting ASR Uncertainty—0
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction—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