Voice based applications are ruling over the era of automation because speech
has a lot of factors that determine a speakers information as well as speech.
Modern Automatic Speech Recognition (ASR) is a blessing in the field of
Human-Computer Interaction (HCI) for efficient communication among humans and
devices using Artificial Intelligence technology. Speech is one of the easiest
mediums of communication because it has a lot of identical features for
different speakers. Nowadays it is possible to determine speakers and their
identity using their speech in terms of speaker recognition. In this paper, we
presented a method that will provide a speakers geographical identity in a
certain region using continuous Bengali speech. We consider eight different
divisions of Bangladesh as the geographical region. We applied the Mel
Frequency Cepstral Coefficient (MFCC) and Delta features on an Artificial
Neural Network to classify speakers division. We performed some preprocessing
tasks like noise reduction and 8-10 second segmentation of raw audio before
feature extraction. We used our dataset of more than 45 hours of audio data
from 633 individual male and female speakers. We recorded the highest accuracy
of 85.44%.

通过应用连续的孟加拉语音，我们提出了一种方法来确定某个地区说话者的地理身份，使用了 Mel 频率倒谱系数（MFCC）和 Delta 特征在人工神经网络上对说话者进行分类，并在特征提取之前对原始音频进行了一些预处理任务。我们的数据集包括 633 个男性和女性说话者的超过 45 小时的音频数据，并获得了 85.44% 的最高准确率。