# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 1 Introduction  
The Bangla language is well-known around the world, and it is the fifth most spoken language on the planet [1]. The population of Bangladesh speak two different varieties of Bangla. Few people speak the local language of their region. The mainstream Bangla language, which is spoken by about 290 million people, is another variety. There are 55 regional languages spoken in Bangladesh's 64 districts. A regional language, also known as a dialect, is a language a child learns organically without the use of written grammar, and that varies by region. It is a characteristic of languages that are widely spoken in a given location [1] that causes morphological differences in the sounds of the ideal language or literary language. Despite regional variations, the Bangla language can be divided into six classes: Bangla, Manbhumi, Vanendri, Rachi, Rangpurti, and Sundarbarani. This study primarily focused on seven regional languages; Khulna, Bogra, Rangpurti, Sylhet, Chittagong, Noakhali, and Mymensingh divisions, which all belong to one of these classes, and one was chosen at random. A person's regional language can be identified by the wave frequency (pronunciation) of a word pronounced in Bangla. On a global scale, the human voice is the most widely used method of communication between machines and humans. Dialects are what bind individuals together. Dialects help people convey their ideas more effectively. As stated by [PERSON] et al. [2], speech recognition is the ability of a machine or computer software to recognize phrases and words in spoken language and translate them into machine-readable form. Voice signals containnot only information about the content of speech, but also about the speaker's identity, emotions, age, gender, and geographical location. In human-computer interaction (HCI), voice signals are also crucial. Identifying characteristics of audio streams automatically has recently been a hot topic of research for the Bangla language [1; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Application development based on consumer regional language, age, or gender, as well as a caller-agent coupling in contact centers that correctly allocates agents according to the caller's identity, are made possible by an accurate and efficient extraction of speaker identification from a voice signal. However, in most of these studies, the speaker's regional language is not categorized or a method for detecting synthetic Bangla speech audio signals is not presented. Several works have been done on creating synthesized Bangla speech. According to [PERSON] [13], [PERSON] [17], and [PERSON] [12], the result is a deficient automatic speech recognition (ASR) and text-to-speech (TTS) system for the Bangla language. Bangla sentence grammar and phonetic construction differ from English sentence grammar and phonetic construction. A typical Bangla sentence has a subject followed by an object and then a verb. Bangla sentences do not use the auxiliary verb, and the subject comes before the object, as it has been pointed out by [PERSON] [6]. Additionally, the preposition must be placed before a noun or else noun-equivalent words must be used when constructing regional Bangla language sentences. It takes a large vocabulary and a lot of phoneme patterns to recognize speech in Bangla. In a detailed work published on the challenges and opportunities for Bangla language speech recognition [18], it was noted that, in order for any system to recognize the features in Bangla speech, it must understand the structure of Bangla language grammatically and phonetically to build a flawless ASR and TTS system. Previous researchers have also encountered language-dependent and language-independent challenges when trying to recognize Bangla speech. For creating a flawless ASR system that produces clean artificial Bangla speech, [PERSON] et al. [18] emphasized the importance of a large grammatical and phonetic database.