To address this issue, a 1D CNN can be designed to operate directly on the speech signal
without needing a spectrogram. 1D convolutions are much less computationally expensive than
2D convolutions because they only operate on one dimension of the input. This reduces the
multiplications and additions required, making the network faster and more efficient. In addition,
1D feature maps require less memory during processing, which is especially important for real-time
applications. A neural network’s memory requirements are proportional to its feature maps’ size.
By using 1D convolutions, the size of the feature maps can be significantly reduced, which can
improve the efficiency of the network and reduce the memory requirements.

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1D CNN.
AReviewofDeepLearningTechniquesforSpeechProcessing