Research on in-car Speech Recognition Based on One Dimensional Convolutional Neural Networks

Convolution neural networks(CNNs) has been the architecture of traditional convolution neural networks is two-dimensional(2D), which can not reflect the one-dimensional characteristic of speech signal. Therefore, a one-dimensional(1D)architecture for speech recognition was proposed, which can better satisfy the temporal variation while retaining band correlation by convolution along the time axis. Experiments of in-car speech recognition demonstrate that 1D CNNs can significantly outperform the 2D CNNs, with recognition rate improvement of 10% to 20%, and the generalization performance in noisy environment of 1D CNNs is also significantly better than the latter.