# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 2 Literature Review  
A comprehensive survey of the literature on state-of-the-art spoken LID, focusing especially on speech features and architectures, was conducted by [PERSON] et al. [15]. Most of the architectures used spectral and acoustic features for spoken LID [16; 17; 18]. However, there are few reported attempts for spoken Arabic language identification among the set of languages. In 1999, [PERSON] et al. [19] proposed a spoken LID system for identifying just two languages: Arabic and English. [PERSON] et al. [20] presented an LSTM-Recurrent Neural Network (LSTM-RNN) architecture for the spoken LID system. The system outperformed a reference I-vector system by 26.00% on a subset of the National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) 2009 corpus for eight languages. Furthermore, the results demonstrated that an LSTM-RNN architecture could identify languages with an accuracy of 70.90%. [PERSON] [16] proposed Fourier Parameter (FP) features for the spoken LID system. The performance of the FP features was analyzed and compared with the legacy Mel Frequency Cepstral Coefficient (MFCC) features. They used the Indian Institute of Technology Kharagpur Multi-lingual Indian Language Speech Corpus (IITKGP-MLILSC) and the Oriental Language Recognition Speech Corpus (AP18-OR) to identify the spoken language. Finally, they developed three architectures with the extracted FP and MFCC features: Support Vector Machine (SVM), feed-forward Artificial Neural Network (ANN), and Deep Neural Network (DNN). The results demonstrated that the proposed FP features effectively recognize different spoken languages from speech signals. A performance improvement was also observed when combining FP and MFCC. [PERSON] et al. [21] investigated the previous architectures for spoken LID systems, which utilize the CNN and CRNN architectures. Additionally, they used a set of seven languages. Despite the increasing complexity of the architectures, it was successful, achieving an accuracy of 71.00% for the CNN architecture and 83.00% for the CRNN architecture. [PERSON] and [PERSON] [11] proposed a spoken LID system and investigated another feature that characterizes language-specific properties: speech rhythm. They evaluated two corpora published by different organizations. The first corpus is the Speech Information Technology and Industry Promotion Center (SiTEC). The second corpus is the Mozilla Common Voice (MCV) corpus. Despite the low computational complexity of the system, the results were either poorer than or similar to the performance of traditional approaches, with an error rate of up to 65.66%. [PERSON] et al. [22] developed a hybrid Feature Selection (FS) algorithm using the versatile Harmony Search (HS) algorithm and the Naked Mole-Rat (NMR) algorithm for Human-Computer Interaction (HCI)-based applications by attempting to classify various languages from the three corpora: the Collection of Single Speaker Speech corpus (CSS10) for ten languages, the VoxForge corpus for six languages, and the Madras (IIT-M) speech corpus for ten languages. This study involved extracting their Mel spectrogram features and Relative Spectral Transform-Perceptual Linear Prediction (RESTA-PLP) features, and these features were used as input for five classifiers: SVM, k-Nearest Neighbor (k-NN), Multi-layer Perceptron (MLP), Naive Bayes (NB), and RF. Accuracies of 99.89% on CSS10, 98.22% on VoxForge, and 99.75% on the IIT-Madras speech corpus databases were achieved using RF. [PERSON] et al. [23] presented a system that used hybrid features and ANNlearning architectures for spoken LID of four languages. First, they used RASTA-PLP and MFCC features with RASTA-PLP features. The classifier selected for the spoken LID system was a Feed-Forward Back Propagation Neural Network (FFBPNN). The results indicate better performance with combined MFCC and RASTA-PLP features compared to the individual use of RASTA-PLP features. The proposed classification yielded an accuracy of 95.30%. [PERSON] et al.