Is tonal language a problem for speaker identification (SPID)?

Abstract

The availability of numerous technologies has led to an increase in the usage of bioinformatics in recent years. Siri, Alexa, and other artificial intelligence systems assist us in our daily lives. Voice recognition systems are used to confirm an individual's identity based on particular elements retrieved from his or her voice. In this regard, the current study attempted to assess the proportion of speaker identification in tonal language speaking persons. The study included 20 participants in the age range from 20 to 40 years. All participants were given a few phrases to speak and were recorded. PRAAT software was used to analyze the obtained data. A vector was developed by using the first two formants, which was then utilized to calculate the percentage for speaker identification. From small sample size to bigger sample size, three groups were formed: A-5, B-10, and C-20 speakers. In a lower sample size, results showed a benchmark of 65% for vowel /i:/, which is better for SPID, 60% for /a:/, which is above chance level, and 45% for /u:/, which is below chance level. The authors stated that increasing the sample size has an influence on speaker identification.