Even if speech recognition technology has advanced significantly in recent years, there are still issues with it, especially when it comes to correctly differentiating between male and female voices. The present state of male and female voice recognition systems is examined in this paper, along with the roots of the issues and the approaches used to overcome them. We explore both the physiological and social language aspects of speech production and their effects on the precision of recognition. We also go over how deep neural networks and other machine learning methods can improve gender classification in speech recognition systems. Additionally, we examine the effects of gender bias and methods for reducing it in speech recognition software. This review provides insights into the achievements made in male and female voice recognition as well as future directions by combining the results of previous studies.
speech recognition, male speech, female speech
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