A lightweight ECA-based DCNN approach for speech command recognition.

BACKGROUND: Speech recognition allows the recognition of audio streams. It is a tool used by professionals across a range of industries that require accurate transcriptions. In the context of authentication, speech recognition can be used as a biometric factor to verify a user's identity and can be incredibly helpful for individuals with disabilities, particularly those with speech impairments. This evolving technology enables seamless and intuitive communication that closely resembles human conversation.

METHOD: A lightweight end-to-end deep convolutional neural network with an efficient channel attention framework (LW-DCNN-ECA) is proposed in this work for speech recognition to ease communication and flexibility. We address speech recognition in this work. Our framework involves a layer-modified end-to-end deep CNN with an efficient channel attention (ECA) mechanism to recognize the spoken word for speech recognition. The suggested ECA layer is a computationally efficient module employed in lightweight deep convolutional neural networks to improve overall model performance.

RESULTS: In this work, the suggested framework is tested for speech recognition employing the standard datasets of speech commands and mini-speech commands. The presented model has obtained a recognition rate of 98.28 % and a loss of 0.5691 for the mini-speech commands dataset, and similarly for the speech commands dataset, it has obtained a recognition rate of 99.98 % and a loss of 0.2634. The performance of the recommended framework is validated using the larger volume Fisher's corpus and the system robustness is tested using the CHiME-4 corpus.