HyperAI

Keyword Spotting On Google Speech Commands

Métriques

Google Speech Commands V2 35

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif
Nom du modèleGoogle Speech Commands V2 35
hts-at-a-hierarchical-token-semantic-audio98.0
broadcasted-residual-learning-for-efficient-
work-in-progress-linear-transformers-for99.1
importantaug-a-data-augmentation-agent-for95
learning-efficient-representations-for-397.0
hello-edge-keyword-spotting-on-
effective-combination-of-densenet-andbilstm-
hello-edge-keyword-spotting-on-
a-neural-attention-model-for-speech-command93.9
matchboxnet-1d-time-channel-separable-1-
efficient-keyword-spotting-using-time-delay-
end-to-end-keyword-spotting-using-neural-
multi-layer-attention-mechanism-for-speech-
hello-edge-keyword-spotting-on-
hello-edge-keyword-spotting-on-
ast-audio-spectrogram-transformer98.11
towards-on-device-keyword-spotting-using-low98.60
temporal-convolution-for-real-time-keyword-
ssamba-self-supervised-audio-representation97.4
keyword-transformer-a-self-attention-model96.95±0.14
attention-free-keyword-spotting97.56
end-to-end-audio-strikes-back-boosting98.15
wav2kws-transfer-learning-from-speech-
training-keyword-spotters-with-limited-and-
streaming-keyword-spotting-on-mobile-devices-
micronets-neural-network-architectures-for-
subspectral-normalization-for-neural-audio-
hello-edge-keyword-spotting-on-
pate-aae-incorporating-adversarial-
keyword-transformer-a-self-attention-model97.74 ±0.03
keyword-transformer-a-self-attention-model97.69 ±0.09
training-keyword-spotters-with-limited-and-
decentralizing-feature-extraction-with-
subspectral-normalization-for-neural-audio-
subspectral-normalization-for-neural-audio-
neural-architecture-search-for-keyword-
hello-edge-keyword-spotting-on-
edgecrnn-an-edgecomputing-oriented-model-of-
convmixer-feature-interactive-convolution-
howl-a-deployed-open-source-wake-word-
masked-modeling-duo-learning-representations98.5
neural-architecture-search-for-keyword-