# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 1 Introduction
One has to use a database with a large vocabulary and phoneme patterns in the Bangla language in order to build an ASR [7; 19] system that works flawlessly for the Bangla language. There are no public or private databases available for Bangla speech that contain extensive jargon. This is one of the many reasons why research carried out over the past decade on recognition features in Bangla speech has failed to investigate regional language during Bangla speech feature classification because of database limitations. There is a comprehensive study of Bangla speech recognition, presented in [14] classification of Bengali accent using deep learning (DL). Hence, the authors failed to build or use the broad corpus necessary to distinguish regional dialects, receiving an accuracy score of 86%. The authors in [20] used neural networks to detect hate speech in social media and achieved an accuracy rate of 77 percent. [PERSON], [PERSON], and [PERSON] in [13] reported an accuracy score of 98.16% for the classification of abusive words in Bangla speech by using VGG-16. The Deep CNN model developed by [PERSON] et al. in [5] was able to classify Bangla spoken digits with 98.3% accuracy. For unsupervised learning of sparse and temporal hierarchical features to audio signals, autoencoder neural networks were used [21; 22; 23; 24; 25; 26; 27; 28]. In [21], the authors used a convolutional autoencoder along with Mel Frequency Energy Coefficients (MFEC) data to identify anomalies in different types of machine sounds. Their method produced better results than the baseline. Researchers previously developed a variety of methods for recognizing the gender and age of speakers in Bangla. They focused on two key parts: identifying the optimal features and building a model that is able to recognize those features across various types of speech communication. They achieved an accuracy of 90% for gender and age classification. [PERSON] et al. [29] built an end-to-end CNN with a multi-attention module, able to extract the salient spatial and temporal features and recognize age and gender from speech signals. Due to the use of MFECs input data, the authors in [30] used multi-layers perceptron (MLP) to learn the gender and speaker identity features and it outperformed the CNNs model [29]. To the best of our knowledge, the classification of dialects in Bangla language and detection of synthesized Bangla speech has never been done. Therefore, the previous work [13] influenced our proposed model for Bangla audio classification of regional language in Bangla speech based on acoustic features and for detecting artificial Bangla speech from audio signals in this paper. The understanding of how to approach this problem was gained from the work done by authors in [31; 32] to classify regional languages from American English and Chinese speech. The proposed method uses convolutional autoencoders stacked with MLELMs to locate the important information from MFECs and recognize it efficiently from synthesized audio and dialect from Bangla speech. The model also outperforms accuracy scores for dialect, age, and gender achieved by previous researchers. Furthermore, the model addresses the issue of a limited Bangla speech dataset. From the seven dialects of the division above, we create a database containing extensive jargon and phoneme patterns. Over 100,000 Bangla spoken utterances were recorded within the institution. There is no ambiguity in the Bangla statement made by the speakers. The input signals are labeled according to the class to which they belong. Afterward, we discuss the proposed approach for recognizing the speaker's regional language and audio authenticity (machine or actual voice), as well as extensive testing to confirm the recommended system's performance on current models and publicly available datasets, as well as future work. The rest of this paper is organized as follows.