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Efficient keyword spotting by capturing long-range interactions with temporal lambda networks
Abstract
Models based on attention mechanisms have shown unprecedented speech recognition performance. However, they are computationally expensive and unnecessarily complex for keyword spotting, a task targeted to small-footprint devices. This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task. We propose a novel ResNet-based model by swapping the residual blocks by temporal Lambda layers. Furthermore, the proposed architecture is built upon uni-dimensional temporal convolutions that further reduce its complexity. The presented model does not only reach state-of-the-art accuracies on the Google Speech Commands dataset, but it is 85% and 65% lighter than its Transformer-based (KWT) and convolutional (ResNet15) counterparts while being up to 100× faster. To the best of our knowledge, this is the first attempt to explore the Lambda framework within the speech domain and therefore, we unravel further research of new interfaces based on this architecture.Peer ReviewedPostprint (author's final draft- Conference lecture
- Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
- Speech processing systems
- Automatic speech recognition
- Keyword spotting
- Lambda networks
- Speech recognition
- Complex networks
- Network architecture
- Speech recognition
- Attention mechanisms
- Foot-print devices
- Keyword spotting
- Lambda network
- Lambda's
- Long range interactions
- Longer-range interaction
- Model-based OPC
- Small footprints
- Speech recognition performance
- Convolution
- Processament de la parla
- Reconeixement automàtic de la parla