# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 4 Conclusions  
In this paper, a dataset was presented with seven regional Bangla languages. In addition, a Stacked Convolution Autoencoder followed by MFECs for the classification of synthesized voices and regional Bangla languages was proposed. The propopsed method can extract essential features from unsupervised data and classify them accordingly. From the given input data, the SCAE identifies relevant features for class labeling and produces detailed feature maps. The MLELM networks in the suggested method learn from the training data to produce multi-label classification in a single pass. We used two MLELM \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Speech** & \multicolumn{2}{c}{**[PERSON] et al. [35]**} & \multicolumn{2}{c}{**[PERSON] [29]**} & \multicolumn{2}{c}{**SCAE-MLELMs**} \\ \hline  & & **AUC (\%)** & **pAUC (\%)** & **AUC (\%)** & **pAUC (\%)** & **AUC (\%)** & **pAUC (\%)** \\ \hline Audio Type & Bangla & 67.57 & 55.24 & 78.16 & 64.24 & 91.24 & 87.12 \\  & English & 87.45 & 73.15 & 89.78 & 82.57 & 92.75 & 86.74 \\ \hline Dialect & Bangla & 52.47 & 42.18 & 61.41 & 59.78 & 89.75 & 83.12 \\  & English & 60.42 & 57.48 & 68.48 & 55.46 & 89.76 & 86.14 \\ \hline Gender & Bangla & 69.40 & 55.94 & 84.15 & 77.42 & 92.00 & 87.45 \\  & English & 89.73 & 72.49 & 91.42 & 96.87 & 91.42 & 89.48 \\ \hline Age & Bangla & 78.69 & 63.54 & 86.44 & 78.62 & 89.76 & 82.62 \\  & English & 83.45 & 73.44 & 81.46 & 77.48 & 89.41 & 86.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance Results of existing methods; [PERSON] et al. [35]: Deep CNN, and [PERSON] [29]; Multi-attention module CNN model for MFCCs data type. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Speech** & \multicolumn{2}{c}{**[PERSON] et al. [35]**} & \multicolumn{2}{c}{**[PERSON] [29]**} & \multicolumn{2}{c}{**SCAE-MLELMs**} \\ \hline  & & **AUC (\%)** & **pAUC (\%)** & **AUC (\%)** & **pAUC (\%)** & **AUC (\%)** & **pAUC (\%)** \\ \hline Audio Type & Bangla & 76.15 & 65.73 & 87.41 & 78.24 & 92.11 & 89.12 \\  & English & 79.25 & 64.65 & 89.88 & 72.11 & 93.70 & 84.97 \\ \hline Dialect & Bangla & 66.48 & 54.18 & 76.48 & 62.18 & 92.48 & 93.17 \\  & English & 68.42 & 56.38 & 81.42 & 75.40 & 90.45 & 82.75 \\ \hline Gender & Bangla & 72.11 & 65.19 & 86.12 & 73.14 & 91.75 & 83.46 \\  & English & 89.73 & 72.14 & 88.25 & 79.48 & 91.10 & 80.74 \\ \hline Age & Bangla & 83.45 & 72.38 & 83.47 & 78.34 & 90.29 & 89.26 \\  & English & 85.16 & 76.34 & 80.42 & 79.28 & 88.49 & 86.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance Results of existing methods; [PERSON] et al. [35]: Deep CNN, and [PERSON] [29]; Multi-attention module CNN model for MFECs data type. networks because the first performs soft classification scores and soft labels. The second MLELM network matches the soft label to hard labels. We conducted extensive training and testing to evaluate the performance, efficiency, and robustness of the system. The suggested method outperforms the existing algorithms ([PERSON] et al. (2019), a Deep CNN model, and [PERSON] (2018), a multi-attention module CNN model) with an accuracy score of 91%, 89%, 89%, 92% for synthesized/original audio type, dialect, age, and gender classification, respectively for Bangla Speech, for the spectrogram input data type. While for MFECs input format the accuracy scores are synthesized/original audio type, 92%, dialect, 92%, age 90%, gender 91%. Consequently, MFEC's data input format is more reliable when it comes to recognizing relevant salient features from audio inputs. In addition, the proposed model can improve the classification accuracy score for dialect class to 95% by using detailed feature maps produced from the SCAE, which produces the correlated acoustic feature patterns between dialect class and age. As aging has a physiological change that impacts the processing of auditory feedback in the brain. Hence with the help of MLELM networks, the multi-label data was used to create correlated feature maps of the data.