This article surveys convolution-based models - convolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recognition, speaker identification, emotion recognition, and speech enhancement applications. Through comparative training cost assessment, model size, accuracy and speed assessment, we compare the strengths and weaknesses of each model, identify potential errors and propose avenues for further research, emphasising the central role it plays in advancing applications of speech technologies.

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding task-specific contexts. To address these issues, we propose a system called _DemoCraft_, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: _MBPP_ and _Humaneval_. Our experimental results demonstrate that the proposed system achieves an approximate _2x_ increase in the _pass@k_ metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: _correctness@k_ and _similarity@k_. Our empirical studies indicate that our system attains nearly a _3x_ improvement in these metrics as well.