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Found 3 papers, 1 papers with code
Training dynamic models using early exits for automatic speech recognition on resource-constrained devices
In self-attention models for automatic speech recognition (ASR), early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational resources and ASR performance demands.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Improving the Intent Classification accuracy in Noisy Environment
Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models.
Scaling strategies for on-device low-complexity source separation with Conv-Tasnet
Given the modular nature of the well-known Conv-Tasnet speech separation architecture, in this paper we consider three parameters that directly control the overall size of the model, namely: the number of residual blocks, the number of repetitions of the separation blocks and the number of channels in the depth-wise convolutions, and experimentally evaluate how they affect the speech separation performance.
