On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms

Abstract

(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the implications of such approaches towards the development of novel hearing aid features (i.e., voice familiarity detection). To do this, we built and tested a Convolutional Neural Network (CNN) Model for the identification and classification of a series of voices, namely the 10 cast members of the popular television show “Modern Family”. (2) Methods: Representative voice samples were selected from Season 1 of Modern Family (N = 300; 30 samples for each of the classes of the classification in this model, namely Phil, Claire, Hailey, Alex, Luke, Gloria, Jay, Manny, Mitch, Cameron). The audio samples were then cleaned and normalized. Feature extraction was then implemented and used as the input to train a basic CNN model and an advanced CNN model. (3) Results: Accuracy of voice classification for the basic model was 89%. Accuracy of the voice classification for the advanced model was 99%. (4) Conclusions: Greater familiarity with a voice is known to be beneficial for speech recognition. If a hearing aid can eventually be programmed to recognize voices that are familiar or not, perhaps it can also apply familiar voice features to improve hearing performance. Here we discuss how such machine learning, when applied to voice recognition, is a potential technological solution in the coming years.

Keywords

• machine learning
• voice classification
• hearing aid
• voice familiarity