M.S. (Master of Science)
Department of Electrical Engineering
Pronunciation is essential in language learning. Standard English speakers could pronounce in the right way, because their pronunciation procedure is correct. The aim of this research is to see how an artificial intelligence program can be developed to provide feedback to people while they learn to generate accurate phoneme /r/. The audio signal will first be detected, after which features of audio signal will be extracted, and finally, classification will be performed. This suggested algorithm and method may be applied to other related topics. This thesis proposes new features and uses them to classify standard English speakers and non-standard English speakers. The first feature is tracking the amplitude change of fundamental frequencies. This algorithm calculates the derivative of frequency change over time as a new feature. The second feature applies the Schrödinger equation and the infinite square well model's boundary value to calculate the audio signal's wave function to modify the Mel Frequency Cepstral Coefficients (MFCC). Compared with the results that use original Mel Frequency Cepstral Coefficients (MFCC) as the feature, the experiment results show that the proposed methods are effective.
Zhuang, Mutian, "New Features For Speech Processing Standard Pronunciation Classification" (2021). Graduate Research Theses & Dissertations. 7806.
Northern Illinois University
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