Keyword Spotting On Google Speech Commands

Métriques

Google Speech Commands V2 35

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Google Speech Commands V2 35	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

0 of 42 row(s) selected.