Gender Classification Based on the Non-Lexical Cues of Emergency Calls with Recurrent Neural Networks (RNN)

Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (human-computer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement. Until now, gender classification has been made using differences in the spectral characteristics of males and females. We assumed that a neutral margin exists between the male and female spectral range. This margin causes misclassification of gender. To address this limitation, we studied three non-lexical speech features (fillers, overlapping, and lengthening). From the statistical analysis, we found that overlapping and lengthening are effective in gender classification. Next, we performed gender classification using overlapping, lengthening, and the baseline acoustic feature, Mel Frequency Cepstral Coefficient (MFCC). We have tried to achieve the best results by using various combinations of features at the same time or sequentially. We used two types of machine-learning methods, support vector machine (SVM) and recurrent neural networks (RNN), to classify the gender. We achieved 89.61% with RNN using a feature set including MFCC, overlapping, and lengthening at the same time. Also, we have reclassified using non-lexical features with only data belonging to the neutral margin which was empirically selected based on the result of gender classification with only MFCC. As a result, we determined that the accuracy of classification with RNN using lengthening was 1.83% better than when MFCC alone was used. We concluded that new speech features could be effective in improving gender classification through a behavioral approach, notably including emergency calls.