In this research, an advanced system getting-to-know model is being carried out to properly examine and analyze multiple Bangladeshi vocal cues to diagnose gender correctly. This study is implemented in quite a few domain names, including customer service, where determining the gender of a caller can provide extra-personalized interactions. Additionally, voice-activated assistants use it to customize responses and beautify the consumer experience. To beautify provider shipping and offer demographic insights and gender recognition the use of voice evaluation is also utilized in safety systems, transcription services and sociolinguistic research. Voice is stricken by environmental way of life elements such as smoking, acid reflux sickness, air pollutants, warm weather weight reduction, city air pollutants and the horrible effect of energy on Bangladeshi fitness. In this study, we obtained data on voices from male and female participants living in Bangladesh. To provide a consistent and convenient method of research, we first converted each recording to Waveform Audio File Format (WAV). We then extracted the most significant voices from these WAV recordings and converted them to statistical data. Then, we preprocessed this statistical information to put it together for in-depth analysis. After preprocessing, we used report visualization to understand the traits and patterns observed within the voice recordings. This holistic approach enables a complete assessment of voice data to gain the goals of our study. So, the idea of this venture is to train a machine learning model with updated information processing techniques which can be expected should be gender according to voice notes. We goal to establish a dependable gender identification set of rules based on modern-day findings and big-scale facts.

Keywords: Voice analysis, Gender classification, Acoustic features, Machine learning, Vocal biometrics, Machine learning classification, Bengali language dataset, Gender recognition system, Random forest, Logistic regression, Bangla audio data, Audio data.

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Source of Funding:

This study did not receive any grant from funding agencies in the public, commercial, or not–for–profit sectors.

Competing Interests Statement:

The authors declare no competing financial, professional, or personal interests.

Consent for publication:

The authors declare that they consented to the publication of this study.

Authors' contributions:

All the authors made an equal contribution in the Conception and design of the work, Data collection, Simulation analysis, Drafting the article, and Critical revision of the article. All the authors have read and approved the final copy of the manuscript.

Availability of data and material:

Authors are willing to share data and material according to the relevant needs.