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Keyword Spotting On Google Speech Commands

Métriques

Google Speech Commands V2 35

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
HTS-AT98.0HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection
BC-ResNet-8-Broadcasted Residual Learning for Efficient Keyword Spotting
WaveFormer99.1Work in Progress: Linear Transformers for TinyML
ImportantAug95ImportantAug: a data augmentation agent for speech
TripletLoss-res1597.0Learning 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 RNN93.9A 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 Transformer98.11AST: Audio Spectrogram Transformer
QNN98.60Towards on-Device Keyword Spotting using Low-Footprint Quaternion Neural Models
TC-ResNet14-1.5-Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
SSAMBA97.4SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
KWT-196.95±0.14Keyword Transformer: A Self-Attention Model for Keyword Spotting
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Keyword Spotting On Google Speech Commands | SOTA | HyperAI