In response to the demands for small model size and low latency in speech command recognition applications with small vocabularies in smart homes,this paper designs two lightweight Chinese end-to-end command recognition models based on neural networks and connectionist temporal classification(CTC).Model lightness is achieved by simplifying network layers and structures,and CTC algorithm is introduced for end-to-end training and decoding using Chinese characters as modeling units,addressing the data prealignment problem.Finally,Comparative evaluations on the Aishell-I dataset and cus-tom corpora demonstrate that the CNN-CTC model,with a 350kb model size,5ms runtime,5.02%word error rate,and 92.0%intent recognition accuracy,is more suitable for small-vocabulary speech command recognition applications.
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