Ph.D

Noise recognition plays an essential role in human-computer interaction and various technological applications. However, identifying individual speakers remains a significant challenge, especially in diverse and acoustically challenging environments. This paper presents the Enhanced Multi-Layer Convolutional Neural Network (EML-CNN), a novel approach to improve automated speaker recognition from audio speech. The EML-CNN architecture features multiple convolutional layers and a dense block, finely tuned to extract unique voice signatures from English speech samples. The proposed model, trained with an expanded dataset from twelve distinct speakers, significantly broadens its capacity to identify diverse speech patterns.Advanced audio augmentation techniques were employed to augment the dataset's variability, including adding white Gaussian noise, signal manipulation (shifting and stretching), frequency modulation, tempo adjustment, and pitch variation. These methods significantly increased the dataset's diversity, enhancing the EML-CNN's robustness. Hyperband tuning and extensive parameter optimization were applied to improve the model's performance and prevent overfitting.Our evaluation results demonstrate that the EML-CNN achieves an outstanding accuracy of 99.5\% on a specialized speech dataset, maintaining high performance with 96.2\% accuracy on the challenging THUYG-20+ benchmark. These findings highlight the EML-CNN's superior performance over traditional audio classification and machine learning methods, marking a significant advancement in automated speaker recognition technology.

噪声识别在人机交互与各类技术应用中均发挥着至关重要的作用。然而，对单一说话人进行识别仍是一项重大挑战，尤其是在多样化且声学环境复杂的场景中。本文提出了一种可提升语音自动说话人识别性能的新型方法——增强型多层卷积神经网络（Enhanced Multi-Layer Convolutional Neural Network，EML-CNN）。该EML-CNN架构包含多层卷积层与一个稠密块，经过精细调优以从英语语音样本中提取独特的语音特征标识。本模型基于12名不同说话人的扩展数据集进行训练，大幅提升了其识别多样化语音模式的能力。研究采用了先进的音频数据增强技术以提升数据集的多样性，具体包括添加高斯白噪声、信号处理（移位与拉伸）、频率调制、节拍调整以及音高变换等手段。这些方法有效提升了数据集的多样性，进而增强了EML-CNN的鲁棒性。本研究采用Hyperband调优策略与全方位参数优化方法，以优化模型性能并避免过拟合。本次评估结果显示，EML-CNN在专用语音数据集上取得了99.5%的卓越准确率，在极具挑战性的THUYG-20+基准测试集上也保持了96.2%的高精度性能。上述研究结果表明，相较于传统音频分类与机器学习方法，EML-CNN拥有更优异的性能，这标志着自动说话人识别技术取得了重大进展。

搜集汇总

背景与挑战

背景概述

该数据集是一个名为'English_Scripted_Speech_Corpus'的英语脚本语音语料库，主要用于说话人识别研究，与Enhanced Multi-Layer Convolutional Neural Network (EML-CNN)模型相关联。数据集包含来自12位不同说话者的语音样本，并经过音频增强处理以提高多样性，在评估中实现了99.5%的高准确率，适用于数字信号处理、机器学习和计算机视觉领域。