Voice-Based Gender Recognition Model Using FRT and Light GBM

Voice-based gender recognition is vital in many computer-aided voice analysis applications like Human-Computer Interaction, fraudulent call identification, etc. A powerful feature is needed for training the machine learning model to discriminate a gender as male or female from a voice signal. This work proposes the use of a gradient boosting model in conjunction with a novel Cumulative Point Index (CPI) feature computed by Forward Rajan Transform (FRT) for gender recognition from voice signals. Firstly, voice signals are preprocessed to remove the nonsignificant silence period and are further framed and windowed to make them stationary. Then CPI is computed using the first coefficients of FRT and concatenated to form a feature set, and it is used to train the Light Gradient Boosting Machine (LightGBM) to recognize the gender. This approach provides better accuracy and faster training compared with the state of the art techniques. Experimental results show the primacy of the FRTCPI over other standard features used in the literature. It is also shown that the proposed features, in combination with LightGBM, provide better accuracy of 95.26% with a less computational time of 2.25 s over the challenging large datasets like Speech Accent Archive, Voice Gender Dataset, Common Voice, and Texas Instruments/Massachusetts Institute of Technology corpus

Gender Recognition by Voice Using an Improved Self-Labeled Algorithm

Speech recognition has various applications including human to machine interaction, sorting of telephone calls by gender categorization, video categorization with tagging and so on. Currently, machine learning is a popular trend which has been widely utilized in various fields and applications, exploiting the recent development in digital technologies and the advantage of storage capabilities from electronic media. Recently, research focuses on the combination of ensemble learning techniques with the semi-supervised learning framework aiming to build more accurate classifiers. In this paper, we focus on gender recognition by voice utilizing a new ensemble semi-supervised self-labeled algorithm. Our preliminary numerical experiments demonstrate the classification efficiency of the proposed algorithm in terms of accuracy, leading to the development of stable and robust predictive models