The determination and classification of natural language based on specified content and data set involves a process known as spoken language identification (LID). To initiate the process, useful features of the given data need to be extracted first in a mature process where the standard LID features have been previously developed by employing the use of MFCC, SDC, GMM and the i-vector-based framework. Nevertheless, optimisation of the learning process is still required to enable a comprehensive capturing of the extracted features’ embedded knowledge. The training of a single hidden layer neural network can be done using the extreme learning machine (ELM), which is an effective learning model for conducting classification and regression analysis. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. This study employs ELM as the LID learning model centred upon the extraction of the standard features. The enhanced self-adjusting extreme learning machine (ESA–ELM) is one of the ELM’s optimisation techniques which has been chosen as the benchmark and is enhanced by adopting a new alternative optimisation approach (PSO) instead of (EATLBO) in terms of achieving high performance. The improved ESA–ELM is named particle swarm optimisation–extreme learning machine (PSO–ELM). The generated results are based on LID with the same benchmarked data set derived from eight languages, which indicated the superior performance of the particle swarm optimisation–extreme learning machine LID (PSO–ELM LID) with an accuracy of 98.75% in comparison with the ESA–ELM LID which only achieved 96.25%.

中文翻译：

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基于粒子群优化的口语识别——极限学习机方法

基于指定内容和数据集的自然语言的确定和分类涉及称为口语识别 (LID) 的过程。为了启动该过程，需要首先在成熟的过程中提取给定数据的有用特征，其中标准 LID 特征先前已通过使用 MFCC、SDC、GMM 和基于 i-vector 的框架开发。尽管如此，仍然需要优化学习过程以全面捕获提取的特征的嵌入知识。单隐层神经网络的训练可以使用极限学习机（ELM）来完成，这是一种进行分类和回归分析的有效学习模型。然而，这个模型的学习过程并不完全有效（即 优化）由于在输入隐藏层内随机选择权重。本研究采用 ELM 作为 LID 学习模型，以提取标准特征为中心。增强型自调整极限学习机 (ESA–ELM) 是 ELM 的优化技术之一，被选为基准，并通过采用新的替代优化方法 (PSO) 而不是 (EATLBO) 在实现高表现。改进后的 ESA-ELM 被命名为粒子群优化-极限学习机 (PSO-ELM)。生成的结果基于 LID，具有来自八种语言的相同基准数据集，这表明粒子群优化 - 极限学习机 LID (PSO-ELM LID) 的性能优越，精度为 98。