# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 3 Results and Discussion
### Dialect  
Bangla dialects are classified into seven categories: Khulna, Bogra, Rangpur, Sylhet, Chittagong, Noakhali, and Mymensingh. According to Table 4, the highest values obtained for Bangla speech were 78% precision, 78% recall, and 72% F1-score. For Bangla speech, the mean recognition accuracy is 75%. English is a three-category classification problem; Asian (Banglades, India, Pakistan, China, Korean), American and European (United Kingdom, Germany, Russia) and the precision, recall, F1-score and mean accuracy were 81%, 88%, 85%, and 81%, respectively.  
The larger the variation in dialect type, the better the recognition rate. Because all of the regional languages are spoken in Bangla in the Bangla speech dataset, it can be difficult to distinguish the input audio at a high rate. The situation is different with English, where the dialects are quite diverse, making the process of recognition relatively straightforward. Figure 6 provides the confusion matrix obtained from the model prediction for dialect classification. M, N, C, S, R, B, and K stand for Mymensingh, Noakhali, Sylhet, Rangpur, Bogra, and Khulna, respectively. In English, European, American, and Asian are denoted with the following keywords: EU, AM, AS, respectively. In terms of accuracy, Noakhali (78%) is the best followed by Bogra (66%). As a result, the proposed model confused Bogra with Rangpur and Sylhet with Chittagong, 23% and 32%, respectively, incorrectly predicted. There is a significant amount of confusion due to the similarity of the acoustic features, frequency, and intensity between the words used in those regional parts [9]. There are also similarities between American and European dialects when it comes to English speech dialect prediction since they have a few similar words.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{6}{c}{**Bangla Speech**} & \multicolumn{6}{c}{**English Speech**} \\ \hline
**Class Group** & **P** & **R** & **FS** & **Class Group** & **P** & **R** & **FS** \\ \hline Khulna & 0.67 & 0.55 & 0.64 & Asian & 0.61 & 0.57 & 0.70 \\ Bogra & 0.78 & 0.66 & 0.72 & American & 0.76 & 0.88 & 0.76 \\ Rangpur & 0.66 & 0.52 & 0.65 & European & 0.81 & 0.44 & 0.85 \\ Sylhet & 0.50 & 0.54 & 0.58 & & & & \\ Chittagong & 0.66 & 0.70 & 0.57 & & & & \\ Nokhali & 0.72 & 0.78 & 0.70 & & & & \\ Mymensingh & 0.45 & 0.55 & 0.64 & & & & \\ \hline
**Accuracy** & \multicolumn{6}{c}{**0.75**} & \multicolumn{6}{c}{**0.81**} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification Results of Dialect for both Bangla and English Speech Precision (P), Recall (R), and f1-score (FS) using the SCAE-MLELMs model.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{6}{c}{**Bangla Speech**} & \multicolumn{6}{c}{**English Speech**} \\ \hline
**Class Group** & **P** & **R** & **FS** & **Class Group** & **P** & **R** & **FS** \\ \hline Original & 0.90 & 0.94 & 0.93 & Original & 0.94 & 0.95 & 0.94 \\ Synthesized & 0.89 & 0.91 & 0.90 & Synthesized & 0.93 & 0.97 & 0.93 \\ \hline
**Accuracy** & \multicolumn{6}{c}{**0.93**} & \multicolumn{6}{c}{**0.96**} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification Results of Type of Audio for Bangla and English Speech Precision (P), Recall (R), and F1-score (FS) using the SCAE-MLELMs model.