The authors would like to acknowledge the support of Prince Sultan University for paying the Article Procesfsing Charges (APC) of this publication.

The voice signals of human are a type of acoustic signal that transfers the information about the message or word delivered in the form of speech. The speech of each person has its unique acoustic features. The statistical analysis of such features is critical to the speech recognition. Therefore, this paper aims to identify the speaker through statistical analysis of acoustic features of voice signals. First, the data collection method for speech samples was introduced, the voice signals were divided into three categories, namely, normal voice (NV), lower pitch (LP) and raised pitch (RP), and the effects of the LP and RP on speech were discussed. Then, a feature extraction method was coupled with several classifers to identify the LP and RP for speaker identification. Next, the MFCC, ΔMFCC and ΔΔMFC were adopted to extract the acoustic features. Finally, the proposed method was verified through a speaker identification experiment. The results show that our method can accurately capture the acoustic features of each speaker, and correctly differentiate between the NV, LP and RP. The research results are of great significance to speech recognition and speaker identificaiton.

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