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Speech recognition is a technique that converts human speech signals intotext or words or in any form that can be easily understood by computers orother machines. There have been a few studies on Bangla digit recognitionsystems, the majority of which used small datasets with few variations ingenders, ages, dialects, and other variables. Audio recordings of Bangladeshipeople of various genders, ages, and dialects were used to create a largespeech dataset of spoken '0-9' Bangla digits in this study. Here, 400 noisy andnoise-free samples per digit have been recorded for creating the dataset. MelFrequency Cepstrum Coefficients (MFCCs) have been utilized for extractingmeaningful features from the raw speech data. Then, to detect Bangla numeraldigits, Convolutional Neural Networks (CNNs) were utilized. The suggestedtechnique recognizes '0-9' Bangla spoken digits with 97.1% accuracy throughoutthe whole dataset. The efficiency of the model was also assessed using 10-foldcrossvalidation, which yielded a 96.7% accuracy.

A Convolutional Neural Network Based Approach to Recognize Bangla Spoken Digits from Speech Signal