The effects of acoustic features of speech for automatic speaker recognition

Abstract

Automatic speaker recognition is the task of automatically determining or verifying the identity of a speaker from a recording of his or her speech sample and has been studied for many decades. One of the most important steps of speaker recognition that significantly influences the speaker recognition performance is known as feature extraction. Acoustic features of speech have been researched by many researchers around the world, however, there is limited research conducted on African indigenous languages, South African official languages in particular. This paper presents the effects of acoustic features of speech towards the performance of speaker recognition systems focusing on South African low-resourced languages. This study investigates the acoustic features of speech using the National Centre for Human Language Technology (NCHLT) Sepedi speech data. Acoustic features of speech such as Time-domain, Frequency-domain and Cepstral-domain features are evaluated on four machine learning algorithms: K-Nearest Neighbours (K-NN), two kernel-based Support Vector Machines (SVM), and Multilayer Perceptrons (MLP). The results show that the performance is poor for time-domain features and good for spectral-domain features and even better for cepstral-domain features. However, the combination of these three features resulted in a higher accuracy and and F₁ score of 98%.