Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), -nearest neighbour (KNN), and support vector machine (SVM).

中文翻译：

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说话人识别系统高精度分类器的研制

语音信号丰富了用于生物识别以及性别和情感识别等其他应用的大量功能。背景噪声和混响所体现的通道条件是导致测试和训练数据中特征变化的主要挑战。本文提出了一种语音特征一致、识别准确率高的混合说话人识别模型。通过合并来自语音时域分析的基音频率系数，使用梅尔频谱系数 (MFCC) 的功能得到了改进。为了增强抗噪能力，我们提出了一种通过优化粒子群优化（OPSO）算法调整的单隐层前馈神经网络（FFNN）。所提出的模型使用不同级别的自适应高斯白噪声 (AWGN) (0-50 dB) 的 10 倍交叉验证进行测试。该模型在干净的语音环境中获得了 97.83% 的识别准确率。然而，与其他基线分类器（例如普通 FFNN、随机森林（RF）、最近邻（KNN）和支持向量机（SVM））相比，噪声通道的实现对所提出的模型的影响较小。