Phoneme analysis for multiple languages with fuzzy-based speaker identification

Abstract

Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel-Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C-Means and Fuzzy k-Nearest Neighbours and comparing them with k-Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.