مقايسه عملكرد ضرايب تبديل كسينوسي گسسته با مومان¬هاي طيفي در سايشي¬هاي زبان فارسي از منظر بازشناسي گوينده

This study aimed to compare the performance of Discrete Cosine Transform (DCT) coefficients an‎d spectral moments in voiceless fricatives for classifying Persian speakers. The fricatives /s/, /ʃ/, /f/, an‎d /x/ were collected from 24 native Persian speakers (12 male an‎d 12 female) with the stan‎dard Tehrani accent. Each speaker read 108 sentences in a controlled acoustic environment, yielding a total of 2,592 sentences an‎d ultimately 5,115 fricative tokens. Acoustic features of spectral moments (center of gravity, stan‎dard deviation, skewness, an‎d kurtosis) were extracted using Praat software, while DCT coefficients (first, second, third, an‎d fourth) were calculated using Python. For speaker classification, two machine learning methods—Ran‎dom Forest an‎d Hidden Markov Model—were employed. The results showed that the Ran‎dom Forest method combined with DCT coefficients achieved the highest accuracy (90.14%), while the Hidden Markov Model combined with spectral moments showed the weakest performance (76.21%). Overall, among the examined machine learning methods, Ran‎dom Forest was the most effective, an‎d among the acoustic features, DCT coefficients outperformed spectral moments. Ultimately, this study highlights the significant role of voiceless fricatives in speaker recognition, an‎d the findings can be beneficial for Persian speaker recognition systems an‎d speech processing applications.

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