从统计视角解析卷积模型在语音处理中的应用与优劣
Towards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and its Applications
- 基于统计理论分析卷积神经网络等模型的原理
- 对比不同模型在准确率、速度和参数量上的表现
- 适合语音识别与情感分析研究者参考
本文综述了卷积神经网络（CNN）、Conformer、ResNet 和 CRNN 等卷积架构在语音信号处理中的应用，涵盖语音识别、说话人识别、情感识别与语音增强。通过对比训练成本、模型规模、准确率与推理速度，分析各模型的优势与局限，识别潜在误差，并提出未来研究方向，强调其在推动语音技术发展中的核心作用。
原文摘要 · Abstract (English)
This article surveys convolution-based models including 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, emphasizing the central role it plays in advancing applications of speech technologies.
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