# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 2 Literature Review
[24] suggested a deep learning-based system called FuzzyGCP for spoken LID. This system integrates the classification principles of Deep Convolutional Neural Networks (DCNN), a Deep Dumb Multi-Layer Perceptron (DDMLP), and Semi-Supervised Generative Adversarial Networks (SSGAN). They tested the system in four corpora containing two Indic and two foreign language corpora. As a result, the system achieved an accuracy of 98.00% on the Multi-lingual Corpus of Sentence-Aligned Spoken Utterances (MaSS) corpus. Moreover, it achieved the worst performance of 67.00% on the VoxForge corpus. [PERSON] et al. [25] proposed an RNN Transducer (RNN-T) architecture for the spoken LID system. The system exploits phonetically-aware acoustic features and explicit linguistic features for LID tasks. Experiments were conducted on eight languages from the Multi-lingual LibriSpeech (MLS) corpus. The results showed that the proposed system significantly improved the performance on spoken LID tasks, with relative improvements of 12.00% to 59.00% and 16.00% to 24.00% on in-domain and cross-domain corpora, respectively. Spoken LID systems have been widely investigated using multiple architectures in terms of feature extraction and classifier learning. However, they are limited to most human languages [26; 27]. Additionally, while many researchers have successfully used deep learning techniques to solve spoken LID problems, as presented in [22; 23; 24], the central problem remains that the Arabic language has only received limited attention in the spoken LID research community among the set of languages [19; 28; 29]. Therefore, this article seeks to improve the performance of spoken LID tasks for six spoken languages from industrial countries in conjunction with Arabic [30]. These are seven languages of the ten most in-demand foreign languages across the globe [31]. A summary of the literature on spoken LID systems is presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Reference** & **Year** & **Features** & **Classifiers** & **Corpus** & **Classes** & **Languages** & **Results (\%)** \\ \hline [PERSON] et al. [20] & 2016 & MFCC-SDC & LSTM-RNN & NIST LRE & 8 & \begin{tabular}{c} Dari, English, French, \\ Chinese, Pashto, Russian, \\ Spanish, and Urdu \\ \end{tabular} & Accuracy = 70.90 \\ \hline [PERSON] et al. [2] & 2017 & Spectrogram & CRNN & \begin{tabular}{c} European \\ Parliament \\ Statemens and News Channels \\ on YouTube \\ \end{tabular} & 6 & \begin{tabular}{c} English, French, German, \\ Chinese, Russian, \\ and Spanish \\ \end{tabular} & Accuracy = 91.00 \\ \hline [PERSON] and [PERSON]. [27] & 2018 & DNN-BN & LSTM & API7-OLR & 10 & \begin{tabular}{c} Tibetan, Japanese, Kazakh, \\ Korean, Indonesian, \\ Vietnam, Cantonese, \\ and Russian \\ \end{tabular} & 2018 & DNN-BN & LSTM & API7-OLR & 10 & \begin{tabular}{c} Tibetan, Japanese, Kazakh, \\ Korean, Indonesian, \\ Mandarin, Cantonese, \\ and Russian \\ \end{tabular} & 2018 & DNN-BN & LSTM & API7-OLR & 10 & \begin{tabular}{c} Persian, Vietnamese, \\ Indonesian, Cantonese, \\ Japanese, Kazakh, Korean, \\ and Mandarin \\ \end{tabular} & 2018 & \begin{tabular}{c} \\ Accuracy = 70.80 \\ \end{tabular} \\ \[PERSON]. [16] & 2019 & FP-MFCC & ANN & \begin{tabular}{c} IITKGP-MILSC \\ and API8-OLR \\ \end{tabular} & 10 & \begin{tabular}{c} Russian, Vietnamese, \\ Indonesian, Cantonese, \\ Japanese, Kazakh, Korean, \\ Tibetan, Uyghur, \\ and Mandarin \\ \end{tabular} & 2020 & Rhythm & \begin{tabular}{c} R-Vector with \\ I-Vector \\ \end{tabular} & \begin{tabular}{c} SiTEC \\ Mozilla \\ \end{tabular} & 3 & \begin{tabular}{c} 2 \\ Chinese, English, \\ and Spanish \\ \end{tabular} & 2018 & \
\begin{tabular}{c} English and Korean \\ Chinese, English, \\ and Spanish \\ \end{tabular} & 2020 &
\begin{tabular}{c} ER = 4.73 \\ ER = 47.38 \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Review of previous literature on spoken language identification with results.