# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 3 Results and Discussion
### Comparison between Existing Algorithms  
For each classification category, it is compared with the models developed by [PERSON] et al. [35], a Deep CNN model, and [PERSON] [29], a multi-attention module CNN model. We compare the performance and robustness of the techniques using AUC and pAUC measurements. Table 10 shows the AUC and pAUC values for each class category with spectrogram input data format, while Table 11 shows the MFEC data format for both Bangla and English audios. The average AUC and pAUC matrices for each class category for both  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model No.** & \multicolumn{3}{c}{**Bangla Speech**} & \multicolumn{6}{c}{**English Speech**} \\ \hline  & **Audio Type** & **Brac University** & **Google AudioSet** & **VoxCeleb** & **VoxCeleb** & **Gender/Age** & **Audio Type** & **Dialet** & **Gender/Age** \\ \hline
1 & 76 & 75 & 84 & 67 & 79 & 76 & 87 & 78 & 84 \\
2 & 74 & 86 & 90 & 78 & 81 & 73 & 90 & 82 & 86 \\
3 & 87 & 78 & 89 & 84 & 84 & 90 & 89 & 90 & 92 \\
4 & **94** & **93** & **92** & **95** & **94** & **93** & **95** & **94** & **93** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification Accuracy (%) of the four different SCAE-MLELMs’ architecture on different datasets with input format as MFCCs ; Brac University previous and self-built Bangla Speech dataset and Google Audio-Set and VoxCeleb for English speech dataset were used during the experiment. Numbers in bold represent the highest classification accuracies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model No.** & \multicolumn{3}{c}{**Bangla Speech**} & \multicolumn{6}{c}{**English Speech**} \\ \hline  & **Brac University** & **Google AudioSet** & **VoxCeleb** & **VoxCeleb** & **Gender/Age** & **Audio Type** & **Dialet** & **Gender/Age** \\ \hline
1 & 84 & 87 & 89 & 87 & 88 & 86 & 91 & 92 & 92 \\
2 & **95** & **94** & **94** & **97** & **96** & **94** & **95** & **95** & **93** \\
3 & 78 & 84 & 90 & 84 & 83 & 91 & 90 & 90 & 91 \\
4 & 76 & 79 & 76 & 78 & 76 & 79 & 81 & 82 & 86 \\ \hlinedata formats demonstrate that SCAE-MLELMs model outperforms the current model for both speeches.  
In addition, MFECs data formats had greater AUC and pAUC values than MFCCs data formats for all model types. Compared to other classes, the Deep CNN model [36] has the lowest AUC and pAUC performance values for dialect class labels. For dialect class labels, the Deep CNN model [36] has the lowest AUC and pAUC values compared to other classes. In comparison to the approach in [36], the Multi-attention module CNN model [29] produced some of the best results for a few classification labels; age, gender, and audio type. Existing approaches have difficulty distinguishing dialect in speech regardless of language due to their single-label model structure and inability to learn characteristics that include age and dialect in audio frequency patterns. Employing multi-label extreme learning machine networks, as suggested in the model. Moreover, the existing methods do not perform as well with Bangla speech audio input as they do with English speech audio input. The proposed method performs consistently in both languages.