Gender Detection from Human Voice Using Tensor Analysis
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
Abstract
Speech-based communication is one of the most preferred modes of communication for humans. The human voice contains several important information and clues that help in interpreting the voice message. The gender of the speaker can be accurately guessed by a person based on the received voice of a speaker. The knowledge of the speaker’s gender can be a great aid to design accurate speech recognition systems. GMM based classifier is a popular choice used for gender detection. In this paper, we propose a Tensor-based approach for detecting the gender of a speaker and discuss its implementation details for low resourceful languages. Experiments were conducted using the TIMIT and SHRUTI dataset. An average gender detection accuracy of 91% is recorded. Analysis of the results with the proposed method is presented in this paper.