Speech recognition is a technique that converts human speech signals into
text or words or in any form that can be easily understood by computers or
other machines. There have been a few studies on Bangla digit recognition
systems, the majority of which used small datasets with few variations in
genders, ages, dialects, and other variables. Audio recordings of Bangladeshi
people of various genders, ages, and dialects were used to create a large
speech dataset of spoken '0-9' Bangla digits in this study. Here, 400 noisy and
noise-free samples per digit have been recorded for creating the dataset. Mel
Frequency Cepstrum Coefficients (MFCCs) have been utilized for extracting
meaningful features from the raw speech data. Then, to detect Bangla numeral
digits, Convolutional Neural Networks (CNNs) were utilized. The suggested
technique recognizes '0-9' Bangla spoken digits with 97.1% accuracy throughout
the whole dataset. The efficiency of the model was also assessed using 10-fold
crossvalidation, which yielded a 96.7% accuracy.

该研究使用多年龄、性别和方言的孟加拉人的音频记录创建了大型的 “0-9” 孟加拉数字口语数据集并使用 Mel 频率倒谱系数和卷积神经网络来识别数字，该方法在整个数据集中达到了 97.1％的准确率并使用 10 倍交叉验证进行了评估，得出 96.7％的准确性。