Keyword Spotting On Google Speech Commands

Metrics

Google Speech Commands V2 35

Results

Performance results of various models on this benchmark

		Paper Title	Repository
HTS-AT	98.0	HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection
BC-ResNet-8	-	Broadcasted Residual Learning for Efficient Keyword Spotting
WaveFormer	99.1	Work in Progress: Linear Transformers for TinyML	-
ImportantAug	95	ImportantAug: a data augmentation agent for speech
TripletLoss-res15	97.0	Learning Efficient Representations for Keyword Spotting with Triplet Loss
LSTM	-	Hello Edge: Keyword Spotting on Microcontrollers
DenseNet-BiLTSM	-	Effective Combination of DenseNet andBiLSTM for Keyword Spotting	-
GRU	-	Hello Edge: Keyword Spotting on Microcontrollers
Attention RNN	93.9	A neural attention model for speech command recognition
MatchboxNet-3x2x64	-	MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
TDNN	-	Efficient keyword spotting using time delay neural networks	-
End-to-end KWS model	-	End-to-end Keyword Spotting using Neural Architecture Search and Quantization	-
LSTM	-	Multi-layer Attention Mechanism for Speech Keyword Recognition	-
DNN	-	Hello Edge: Keyword Spotting on Microcontrollers
Basic LSTM	-	Hello Edge: Keyword Spotting on Microcontrollers
Audio Spectrogram Transformer	98.11	AST: Audio Spectrogram Transformer
QNN	98.60	Towards on-Device Keyword Spotting using Low-Footprint Quaternion Neural Models	-
TC-ResNet14-1.5	-	Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
SSAMBA	97.4	SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
KWT-1	96.95±0.14	Keyword Transformer: A Self-Attention Model for Keyword Spotting

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Keyword Spotting On Google Speech Commands

Metrics

Results