# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 2 Materials and Methods
### Experimentation
#### 2.3.1 Feature Extraction  
MFEC features are extracted from audio files at the beginning of this study. To better understand the feature that distinguishes different regional languages, we extract separate data features from the.wav file using standard audio feature extraction metrics. Librosa and the mat-plot python library are used to visualize these values. The metrics used to extract audio features are;  
* displays the maximum amplitude of all the samples in the frame. It helps us determine how loud the signal is. It has although, proven [13] sensitive to outliers but extremely useful for onset detection. * defines the percussive and pitched sound by calculating the number of times a signal crossed the horizontal axis. * calculates the root mean square value of the energy of all sample in single time frame. * \"Brightness\" of the sound. This provides us the frequency bins. Where most of the energy in a given sample is stored. In order to extract information from spectrogram, Short-time Fourier transform (STFT) was applied first before spectral centroid could be performed. This feature can help us determine the difference
between the regional language using the variety of frequency bins that can be found in each dialect. * provides a spectral range around the spectral centroid. If we think of the spectral centroid as the mean of the spectral magnitude distribution then spectral bandwidth can be thought of as the 'Variance' of that mean. The spectral bandwidth gives the idea of how the energy of the given sample is spread throughout all the frequency bands. The distribution of the data features across the seven regional language can be seen in Figures 3 and 4. Where if the energy is spread across the frequency bins, then the value of Spectral Bandwidth will be higher. On the other hand, if the energy is focused on specific frequency bins, then the value of Spectral Bandwidth will be lower. MFEC, however, uses mel-scale, which is a perceptually relevant scale for pitch. The implementation can give up to 39 features, but we only use 13 of them for our work. Because we only have 10,000 samples, there was a chance of over-fitting the neural network model. Figure 4: Distribution of audio features; spectral centroid and spectral bandwidth across the seven regional languages studied in this research. Figure 3: Distribution of audio features; amplitude envelope, zero-crossing rate, and root mean square error across the seven regional languages studied in this research. In order to get more detailed features from the audio sample, segmentation is necessary. Therefore, each 5-s audio segment is divided into 1-second audio segments containing 22,050 quantized samples. The total number of audio files is 50,000 after segmenting the audio files into 1-second segments. MFCC features are extracted by applying Fast Fourier Transform 2048 times and using a hop length of 512 samples from a total of 22050 samples of each 1-second audio segment. From 1 s of audio, 44 segments of 13 MFCC features are extracted. Hence, there are 572 MFEC features per 1 s of audio. These MFEC data features contain all the information that is visualized separately by the audio feature extraction metrics, as described above. We chose to not use them during the training process of the proposed model but only fed the MFEC data input to the model. During the training phase, MFEC and audio labels are fed into the SCAE for feature extraction. In order to minimize the Mean Squared Error (MSE) between the input data and the reconstructed output, the encoder and decoder were trained with a learning rate of 0.001 and Adam Optimizer. The training process is scheduled for 200 epochs; however, after no change in validation loss occurs for 10 epochs, the training process is hauled and the best saved model is used. The convolutional autoencoder specifies the number of layers and decreased the number of features. Iteratively, the model is trained until it recognizes the input completely.