# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 3 Selected Speech Corpus  
The corpus selected for this article is from the Mozilla Common Voice (MCV) corpora. MCV is a multi-lingual corpus of speech intended for speech technology research and development. It is designed for ASR purposes and is also well known for being useful in other domains; e.g., LID and gender classification [34]. Each input in the corpora consists of an individual MP3 file and a corresponding text file [35]. The most recent release includes 87 languages, but the publishers periodically add more voices and languages. Over 200,000 male and female speakers have participated, resulting in 18,243 h of collected audio.  
To achieve scale and sustainability, the MCV project uses crowdsourcing for data collection and validation [36]. As an example use case for MCV, we present our spoken LID experiments using subsets of MCV for seven target languages: Arabic, German, English, Spanish, French, Russian, and Chinese. These are the first experiments undertaken on most of these languages for spoken LID. The aim was to compare the spoken LID performance and outcomes of Arabic with other languages. Table 2 presents the characteristics of the MCV corpus. Table 3 summarizes the experimental corpus.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Year** & **Features** & **Classifiers** & **Corpus** & **Classes** & **Languages** & **Results (\%)** \\ \hline [PERSON] et al. [22] & 2020 & Mel spectrogram-RASTA-PLP & RF & VoxForge & 6 & English, French, German, Italian, Russian, and Spanish & Accuracy = 98.22 \\ \cline{3-8}  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline [PERSON] et al. [21] & 2020 & Mel spectrogram & CRNN & EU Repo & 6 & English, French, German, Greek, Italian, and Spanish & Accuracy = 83.00 \\ \hline [PERSON] et al. [32] & 2020 & MFCC-DFCC & Extra Trees & VoxForge & 5 & Dutch, English, French, German, and Portuguese & Accuracy = 85.71 \\ \hline [PERSON] et al. [24] & 2021 & MFCC-Spectral Bandwidth-Spectral Contrast-Spectral Roll-Off-Spredal Flatness-Spectral Centroid-Polynomial-Tontez & VoxForge & 8 & Basque, English, Finnish, Romanian, Russian, and Spanish & Accuracy = 98.00 \\ \hline [PERSON] et al. [23] & 2021 & MFCC-RASTA-PLP & FFBPNN & New Corpus & 4 & French, German, Italian, and Spanish & Accuracy = 67.00 \\ \hline [PERSON] et al. [33] & 2021 & Log-Mel spectrograms & CNN & Mozilla & 4 & English, Hindi, Malayalam, and Tamil & Accuracy = 95.30 \\ \hline [PERSON] et al. [25] & 2022 & Acoustic-Linguistic & RNN-T & MLS & 4 & Estonian, Tamil, and Mandarin & Accuracy = 80.21 \\ \hline [PERSON] et al. [25] & 2022 & Acoustic-Linguistic & RNN-T & MLS & 8 & Dutch, English, French, German, Polish, Portuguese, Italian, and Spanish & ER = 5.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling Rate & 48 kHz \\ Date & 19 January 2022 \\ Validated Hours & 14,122 \\ Recorded Hours & 18,243 \\ Languages & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mozilla corpus characteristics.