Development of novel automated language classification model using pyramid pattern technique with speech signals

Abstract

Language classification using speeches is a complex issue in machine learning and pattern recognition. Various text and image-based language classification methods have been presented. But there are limited speech-based language classification methods in the literature. Also, the previously presented models classified limited numbers of languages, and few are accents. This work presents an automated handcrafted language classification model. The novel pyramid pattern is presented to extract the features extraction. Also, statistical features and maximum pooling are used to generate the features. We have developed our speech-language classification model using two datasets: (i) created a new big speech dataset containing 14,500 speeches in 29 languages, and (ii) used the VoxForge dataset. The neighborhood component analysis method is used to select the most informative 1000 features from the generated features, and these features are classified using a quadratic support vector machine classifier (QSVM). Our developed method yielded 98.87 +/- 0.30% and 97.12 +/- 1.27% accuracies for our and VoxForge datasets, respectively. Also, geometric mean, average precision, and F1-score evaluation parameters are calculated, and they are presented in the results section. This paper presents an accurate language classification model developed using two big speech-language datasets. Our results indicate the success of the proposed pyramid pattern-based language classification method in classifying various speech languages accurately.