  Due to improvements in artificial intelligence, speaker identification (SI)
technologies have brought a great direction and are now widely used in a
variety of sectors. One of the most important components of SI is feature
extraction, which has a substantial impact on the SI process and performance.
As a result, numerous feature extraction strategies are thoroughly
investigated, contrasted, and analyzed. This article exploits five distinct
feature extraction methods for speaker identification in disguised voices under
emotional environments. To evaluate this work significantly, three effects are
used: high-pitched, low-pitched, and Electronic Voice Conversion (EVC).
Experimental results reported that the concatenated Mel-Frequency Cepstral
Coefficients (MFCCs), MFCCs-delta, and MFCCs-delta-delta is the best feature
extraction method.
